Awesome-FL

2026

AAAI

• A Unified Self-Regulating Training Framework for Federated Deep Reinforcement Learning. [PUB]
• Bi-level Personalization for Federated Foundation Models: A Task-vector Aggregation Approach. [PUB]
• BIQ: Bisection Interval Quantization for Communication-efficient Federated Learning. [PUB]
• Breaking Cross-View Associations: Byzantine Model Poisoning Attack against Vertical Federated Learning. [PUB]
• Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach. [PUB]
• Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs. [PUB]
• Causally-Aware Attribute Completion for Incomplete Federated Graph Clustering. [PUB]
• Class-Aware Active Annotation in Federated Semi-Supervised Learning for Medical Image Classification. [PUB]
• Communication-Efficient Heterogeneous Federated Learning with Sparse Prototypes in Resource-Constrained Environments. [PUB]
• CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation. [PUB]
• DA-DFGAS: Differentiable Federated Graph Neural Architecture Search with Distribution-Aware Attentive Aggregation. [PUB]
• Data Heterogeneity and Forgotten Labels in Split Federated Learning. [PUB]
• Decoupling Shared and Personalized Knowledge: A Dual-Branch Federated Learning Framework for Multi-Domain with Non-IID Data. [PUB]
• Divide, Conquer and Unite: Hierarchical Style-Recalibrated Prototype Alignment for Federated Medical Segmentation. [PUB]
• DoBlock: Blocking Malicious Association Propagation for Backdoor-Robust Federated Learning Under Domain Skew. [PUB]
• Domain-Aware Suppression and Aggregation for Federated DG ReID. [PUB]
• DSFedMed: Dual-Scale Federated Medical Image Segmentation via Mutual Distillation Between Foundation and Lightweight Models. [PUB]
• Enhanced Federated Deep Multi-View Clustering Under Uncertainty Scenario. [PUB]
• Equilibrium-Driven Vertical Federated Learning with Selective Privacy Protection. [PUB]
• EvoFMVC: Trusted Federated Multi-View Clustering with Evolutionary Fusion. [PUB]
• Feature-Aware One-Shot Federated Learning via Hierarchical Token Sequences. [PUB]
• FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models. [PUB]
• FedALT: Federated Fine-Tuning Through Adaptive Local Training with Rest-of-World LoRA. [PUB]
• FedARKS: Federated Aggregation via Robust and Discriminative Knowledge Selection and Integration for Person Re-identification. [PUB]
• FedAU2: Attribute Unlearning for User-Level Federated Recommender Systems with Adaptive and Robust Adversarial Training. [PUB]
• FedBRICK: Structural Bias Aware Heterogeneous Foundation Model Federated Tuning. [PUB]
• FedCD: Towards Consolidated Distillation for Heterogeneous Federated Learning. [PUB]
• FedCure: Mitigating Participation Bias in Semi-Asynchronous Federated Learning with Non-IID Data. [PUB]
• FedDNA: DNA Sequence Reconstruction via Deep Evidential Learning and Personalized Federated Aggregation. [PUB]
• Federated CLIP for Resource-Efficient Heterogeneous Medical Image Classification. [PUB]
• Federated Context-Aware Personalized Recommendation. [PUB]
• Federated Graph-level Clustering Network with Attribute Inference. [PUB]
• Federated Incomplete Multi-View Clustering with Tensorized Low-Rank Constraint. [PUB]
• Federated Learning Playground. [PUB]
• Federated Linear Dueling Bandits. [PUB]
• Federated Vision-Language-Recommendation with Personalized Fusion. [PUB]
• FedLAGC: Towards High Performance System-Heterogeneous Federated Learning via Layer-Adaptive Submodel Extraction and Gradient Correction. [PUB]
• FedMerge: Federated Model Merging for Personalization. [PUB]
• FedPKDA: Personalized Federated Learning with Privacy-Preserving Knowledge Dynamic Alignment. [PUB]
• FedPM: Federated Learning Using Second-order Optimization with Preconditioned Mixing of Local Parameters. [PUB]
• FedP²EFT: Federated Learning to Personalize PEFT for Multilingual LLMs. [PUB]
• FedRNC: Addressing Spatio-Temporal Label Misalignment in Federated Noisy Class-Incremental Learning. [PUB]
• FedSDA: Federated Stain Distribution Alignment for Non-IID Histopathological Image Classification. [PUB]
• FedSDWC: Federated Synergistic Dual-Representation Weak Causal Learning for OOD. [PUB]
• FedSEA-LLaMA: A Secure, Efficient and Adaptive Federated Splitting Framework for Large Language Models. [PUB]
• FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness. [PUB]
• FedSkeleton: Secure Multi-Party Graph Skeleton Construction for Privacy-Preserving Federated Time-Series Forecasting. [PUB]
• FedTopo: Topology-Informed Representation Alignment in Federated Learning Under Non-I.I.D. Conditions. [PUB]
• FILTER: A Framework for Defending Against Backdoor Attacks in Vertical Federated Learning. [PUB]
• Generalizable Heterogeneity-aware Federated Feature and Basic-matrix Consistency Learning. [PUB]
• Generic Adversarial Attack Framework Against Graph-based Vertical Federated Learning. [PUB]
• Good Gradients Poison Your Model: Evading Defenses in Federated Learning via Boundary-adaptive Perturbation. [PUB]
• HealSplit: Towards Self-Healing Through Adversarial Distillation in Split Federated Learning. [PUB]
• Horizontal and Vertical Federated Causal Structure Learning via Higher-order Cumulants. [PUB]
• Incomplete Multi-View Unsupervised Federated Feature Selection via Cooperative Particle Swarm Optimization and Tensor-Aligned Learning. [PUB]
• Inter-Client Dependency Recovery with Hidden Global Components for Federated Traffic Prediction. [PUB]
• Intra-Class Unbiased Prototype Aggregation and Classifier Collaboration for Personalized Federated Learning. [PUB]
• Investigating Social Bias Propagation in Federated Fine-tuning of Large Language Models. [PUB]
• LSHFed: Robust and Communication-Efficient Federated Learning with Locally-Sensitive Hashing Gradient Mapping. [PUB]
• MSCFL: Model Structure-Aware Clustered Federated Learning for System Heterogeneity and Data Drift. [PUB]
• Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization. [PUB]
• MultiKD: Backdoor Defense in Federated Graph Learning via Attention-Guided Multi-Teacher Distillation. [PUB]
• Neuro-Symbolic Federated Learning over Heterogeneous Data-Views: A Structured Approach to Distributive EHR Modelling. [PUB]
• Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models. [PUB]
• Optimal Look-back Horizon for Time Series Forecasting in Federated Learning. [PUB]
• OPTION: An Online Pricing Strategy for Asynchronous Federated Learning Against Free-Riding Attacks. [PUB]
• OursFed: Provable Group Fairness-Aware Federated Learning Against Distrust and Fragility. [PUB]
• PAGE: A Unified Approach for Federated Graph Unlearning. [PUB]
• Personalized Federated Graph-Level Clustering Network. [PUB]
• Personalized Federated Learning with Bidirectional Communication Compression via One-Bit Random Sketching. [PUB]
• Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation. [PUB]
• Poisoning with a Pill: Circumventing Detection in Federated Learning. [PUB]
• PPFL: A Parameter Behavior-Driven Plug-in Personalization Engine for Federated Learning. [PUB]
• Prior Refinement Is Better: Diffusion-Driven Graph Harmonization for Federated Graph Learning. [PUB]
• Re-architecting Personalized Federated Learning for Demanding Edge Environments. [PUB]
• REMISVFU: Vertical Federated Unlearning via Representation Misdirection for Intermediate Output Feature. [PUB]
• Retaliatory Attacks Against Federated Unlearning via Data Leakage. [PUB]
• Ripple Shapley: Data Influence Attribution in One Federated Training Run. [PUB]
• Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?. [PUB]
• SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning. [PUB]
• ShadeEdit: A Utility-Preserving and Defense-Evasive Knowledge Manipulation Attack in Federated LLMs. [PUB]
• SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data. [PUB]
• Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack. [PUB]
• TOFA: Training-Free One-Shot Federated Adaptation for Vision-Language Models. [PUB]
• Topological Federated Clustering via Gravitational Potential Fields Under Local Differential Privacy. [PUB]
• Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework. [PUB]
• Towards Robust Text-Attributed Federated Graph Learning: Multimodal Threats and Defense. [PUB]
• TransFR: Transferable Federated Recommendation with Adapter Tuning on Pre-trained Language Models. [PUB]
• Unlocking Dynamic Inter-Client Spatial Dependencies: A Federated Spatio-temporal Graph Learning Method for Traffic Flow Forecasting. [PUB]
• Venom: Liquid Diffusion-Guided Gradient Inversion for Breaking Differential Privacy in Federated Learning. [PUB]
• AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract). [PUB]
• Federated Cross-Modal Style-Aware Prompt Generation (Student Abstract). [PUB]
• UniVarFL: Uniformity and Variance Regularized Federated Learning for Heterogeneous Data (Student Abstract). [PUB]
• A Dialogue-Based Learning Analytics Framework for Collaborative Game-Based Learning. [PUB]
• Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization. [PUB]
• CL-Guard: Defending DNNs Against Backdoors via Fine-Grained Neuron Analysis and Collaborative Dual-Network Learning. [PUB]
• Collaborative Dual Representations for Semi-Supervised Partial Label Learning. [PUB]
• Collaborative Feature Matching with Progressive Correspondence Learning. [PUB]
• Collaborative Representation Learning for Alignment of Tactile, Language, and Vision Modalities. [PUB]
• Cross-Domain Few-Shot Learning via Multi-View Collaborative Optimization with Vision-Language Models. [PUB]
• DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning. [PUB]
• Do Not Merge My Model! Safeguarding Open-Source LLMs Against Unauthorized Model Merging. [PUB]
• Drift-aware Collaborative Assistance Mixture of Experts for Heterogeneous Multistream Learning. [PUB]
• From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging. [PUB]
• GLOBA: Rethinking Parameter Conflicts in Model Merging. [PUB]
• Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation. [PUB]
• Learning to Deliberate: Meta-policy Collaboration for Agentic LLMs with Multi-agent Reinforcement Learning. [PUB]
• Learning to Generate and Extract: A Multi-Agent Collaboration Framework for Zero-Shot Document-Level Event Arguments Extraction. [PUB]
• LLM Collaboration with Multi-Agent Reinforcement Learning. [PUB]
• M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data. [PUB]
• MedSAMix: A Training-Free Model Merging Approach for Medical Image Segmentation. [PUB]
• MergeDNA: Context-Aware Genome Modeling with Dynamic Tokenization Through Token Merging. [PUB]
• Multi-view Invariance Learning for 3D Scene Graph Pre-training via Collaborative Cross-Modal Regularization. [PUB]
• Outlier Matters: Efficient Long-to-Short Reasoning via Outlier-Guided Model Merging. [PUB]
• RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior. [PUB]
• Rep Deep & Machine Learning: Exemplar-Free Continual Video Action Recognition via Slow-Fast Collaborative Learning. [PUB]
• Revisiting Contrastive Learning in Collaborative Filtering via Parallel Graph Filters. [PUB]
• Think Wise, Collaborate Effectively: A Rationale-Aware LLM-Based Recommender with Reinforcement Learning from Collaborative Signals. [PUB]
• Unifying Multi-View Knowledge for Graph Learning via Model Collaboration. [PUB]
• Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent (Abstract Reprint). [PUB]

AI

• Disentangling data distribution for optimal and communication-efficient federated learning. [PUB]
• Federated neural nonparametric point processes. [PUB]

2025

IJCAI

• Exploiting Label Skewness for Spiking Neural Networks in Federated Learning. [PUB]
• FedHAN: A Cache-Based Semi-Asynchronous Federated Learning Framework Defending Against Poisoning Attacks in Heterogeneous Clients. [PUB]
• Heterogeneous Federated Learning with Scalable Server Mixture-of-Experts. [PUB]
• Pixel-wise Divide and Conquer for Federated Vessel Segmentation. [PUB]
• Universal Backdoor Defense via Label Consistency in Vertical Federated Learning. [PUB]
• Where Does This Data Come From? Enhanced Source Inference Attacks in Federated Learning. [PUB]
• Optimizing Personalized Federated Learning Through Adaptive Layer-Wise Learning. [PUB] [CODE]
• FedDLAD: A Federated Learning Dual-Layer Anomaly Detection Framework for Enhancing Resilience Against Backdoor Attacks. [PUB] [CODE]
• Federated Multi-view Graph Clustering with Incomplete Attribute Imputation. [PUB]
• ADPFedGNN: Adaptive Decoupling Personalized Federated Graph Neural Network. [PUB]
• Approximated Behavioral Metric-based State Projection for Federated Reinforcement Learning. [PUB]
• FissionVAE: Federated Non-IID Image Generation with Latent Space and Decoder Decomposition. [PUB]
• FedBG: Proactively Mitigating Bias in Cross-Domain Graph Federated Learning Using Background Data. [PUB]
• FedCCH: Automatic Personalized Graph Federated Learning for Inter-Client and Intra-Client Heterogeneity. [PUB]
• FedCPD:Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation. [PUB]
• Data Poisoning Attack Defense and Evolutionary Domain Adaptation for Federated Medical Image Segmentation. [PUB]
• Distilling A Universal Expert from Clustered Federated Learning. [PUB]
• CSAHFL:Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks. [PUB]
• FAST: A Lightweight Mechanism Unleashing Arbitrary Client Participation in Federated Learning. [PUB]
• Hypernetwork Aggregation for Decentralized Personalized Federated Learning. [PUB]
• Federated Domain Generalization with Decision Insight Matrix. [PUB]
• Generic Adversarial Attack Framework Against Vertical Federated Learning. [PUB]
• One-shot Federated Learning Methods: A Practical Guide. [PUB]
• Federated Learning at the Forefront of Fairness: A Multifaceted Perspective. [PUB]
• Performance Guaranteed Poisoning Attacks in Federated Learning: A Sliding Mode Approach. [PUB]
• Federated Deconfounding and Debiasing Learning for Out-of-Distribution Generalization. [PUB]
• FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data. [PUB] [CODE]
• An Empirical Study of Federated Prompt Learning for Vision Language Model. [PUB]
• FedCM: Client Clustering and Migration in Federated Learning via Gradient Path Similarity and Update Direction Deviation. [PUB]
• Zero-shot Federated Unlearning via Transforming from Data-Dependent to Personalized Model-Centric. [PUB]
• DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning Under Two-sided Incomplete Information. [PUB]
• Backdoor Attack on Vertical Federated Graph Neural Network Learning. [PUB]
• Federated Low-Rank Adaptation for Foundation Models: A Survey. [PUB]
• Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning. [PUB]
• FedSaaS: Class-Consistency Federated Semantic Segmentation via Global Prototype Supervision and Local Adversarial Harmonization. [PUB]
• A Multi-Granularity Clustering Approach for Federated Backdoor Defense with the Adam Optimizer. [PUB]
• Federated Stochastic Bilevel Optimization with Fully First-Order Gradients. [PUB]
• AdaptPFL: Unlocking Cross-Device Palmprint Recognition via Adaptive Personalized Federated Learning with Feature Decoupling. [PUB]
• Rethinking Federated Graph Learning: A Data Condensation Perspective. [PUB]
• MMGIA: Gradient Inversion Attack Against Multimodal Federated Learning via Intermodal Correlation. [PUB]
• Enhancing the Performance of Global Model by Improving the Adaptability of Local Models in Federated Learning. [PUB]
• Finite-Time Analysis of Heterogeneous Federated Temporal Difference Learning. [PUB]
• Inconsistency-Based Federated Active Learning. [PUB]
• CSAHFL: Clustered Semi-Asynchronous Hierarchical Federated Learning for Dual-layer Non-IID in Heterogeneous Edge Computing Networks. [PUB]
• FedCPD: Personalized Federated Learning with Prototype-Enhanced Representation and Memory Distillation. [PUB]
• Bidirectional Human-AI Collaboration for Equitable Student Performance Prediction via Deep Uncertainty Learning. [PUB]
• Credit Assignment and Fine-Tuning Enhanced Reinforcement Learning for Collaborative Spatial Crowdsourcing. [PUB]
• Cross-modal Collaborative Representation Learning for Text-to-Image Person Retrieval. [PUB]
• Enhancing Mixture of Experts with Independent and Collaborative Learning for Long-Tail Visual Recognition. [PUB] [CODE]

AISTATS

• Optimising Clinical Federated Learning through Mode Connectivity-based Model Aggregation. [PUB] [CODE]
• FedBaF: Federated Learning Aggregation Biased by a Foundation Model. [PUB]
• Global Group Fairness in Federated Learning via Function Tracking. [PUB] [CODE]
• On the Power of Adaptive Weighted Aggregation in Heterogeneous Federated Learning and Beyond. [PUB] [CODE]
• Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents. [PUB] [CODE]
• ADEPT: Hierarchical Bayes Approach to Personalized Federated Unsupervised Learning. [PUB] [CODE]
• Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis. [PUB] [CODE]
• The cost of local and global fairness in Federated Learning. [PUB] [CODE]
• Federated Communication-Efficient Multi-Objective Optimization. [PUB] [CODE]
• Refined Analysis of Constant Step Size Federated Averaging and Federated Richardson-Romberg Extrapolation. [PUB] [CODE]
• Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning. [PUB] [CODE]
• On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients. [PUB] [CODE]
• DPFL: Decentralized Personalized Federated Learning. [PUB] [CODE]
• Unbiased Quantization of the L1 Ball for Communication-Efficient Distributed Mean Estimation. [PUB]

AI

• FedHM: Efficient federated learning for heterogeneous models via low-rank factorization. [PUB]

AAAI

• Learning Together Securely: Prototype-Based Federated Multi-Modal Hashing for Safe and Efficient Multi-Modal Retrieval. [PUB]
• Single-Loop Federated Actor-Critic across Heterogeneous Environments. [PUB]
• Improving Federated Domain Generalization Through Dynamical Weights Calculated from Data Influences on Global Model Update. [PUB]
• FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning. [PUB]
• FedGOG: Federated Graph Out-of-Distribution Generalization with Diffusion Data Exploration and Latent Embedding Decorrelation. [PUB]
• ConFREE: Conflict-free Client Update Aggregation for Personalized Federated Learning. [PUB]
• Personalized Label Inference Attack in Federated Transfer Learning via Contrastive Meta Learning. [PUB]
• Rethinking Byzantine Robustness in Federated Recommendation from Sparse Aggregation Perspective. [PUB]
• Asynchronous Federated Clustering with Unknown Number of Clusters. [PUB]
• Generating Synthetic Data for Unsupervised Federated Learning of Cross-Modal Retrieval. [PUB]
• HaCore: Efficient Coreset Construction with Locality Sensitive Hashing for Vertical Federated Learning. [PUB]
• LoGoFair: Post-Processing for Local and Global Fairness in Federated Learning. [PUB]
• Multifaceted User Modeling in Recommendation: A Federated Foundation Models Approach. [PUB]
• Modeling Inter-Intra Heterogeneity for Graph Federated Learning. [PUB]
• pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning. [PUB]
• First-Order Federated Bilevel Learning. [PUB]
• GAS: Generative Activation-Aided Asynchronous Split Federated Learning. [PUB]
• FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis. [PUB]
• Federated Graph Condensation with Information Bottleneck Principles. [PUB]
• A High-Efficiency Federated Learning Method Using Complementary Pruning for D2D Communication (Student Abstract). [PUB]
• Federated Learning with Sample-level Client Drift Mitigation. [PUB]
• Pilot: Building the Federated Multimodal Instruction Tuning Framework. [PUB]
• Flexible Sharpness-Aware Personalized Federated Learning. [PUB]
• MultiSFL: Towards Accurate Split Federated Learning via Multi-Model Aggregation and Knowledge Replay. [PUB]
• PFedCS: A Personalized Federated Learning Method for Enhancing Collaboration among Similar Classifiers. [PUB]
• Federated Graph Anomaly Detection Through Contrastive Learning with Global Negative Pairs. [PUB]
• Fed-DFA: Federated Distillation for Heterogeneous Model Fusion Through the Adversarial Lens. [PUB]
• Federated Recommendation with Explicitly Encoding Item Bias. [PUB]
• Defending Against Sophisticated Poisoning Attacks with RL-based Aggregation in Federated Learning. [PUB]
• Decentralized Federated Learning with Model Caching on Mobile Agents. [PUB]
• Cluster Based Heterogeneous Federated Foundation Model Adaptation and Fine-Tuning. [PUB]
• FedFSL-CFRD: Personalized Federated Few-Shot Learning with Collaborative Feature Representation Disentanglement. [PUB]
• Reinforcement Active Client Selection for Federated Heterogeneous Graph Learning. [PUB]
• Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness. [PUB]
• Federated Weakly Supervised Video Anomaly Detection with Multimodal Prompt. [PUB]
• Overcoming Heterogeneous Data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach. [PUB]
• Reputation-aware Revenue Allocation for Auction-based Federated Learning. [PUB]
• Learn How to Query from Unlabeled Data Streams in Federated Learning. [PUB]
• Efficient Federated Learning via Clients-to-Server Knowledge Distillation (Student Abstract). [PUB]
• Graph Consistency and Diversity Measurement for Federated Multi-View Clustering. [PUB]
• WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling. [PUB]
• Label-Free Backdoor Attacks in Vertical Federated Learning. [PUB]
• Incongruent Multimodal Federated Learning for Medical Vision and Language-based Multi-label Disease Detection. [PUB]
• FedPIA – Permuting and Integrating Adapters Leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning. [PUB]
• Fair Federated Survival Analysis. [PUB]
• Federated t-SNE and UMAP for Distributed Data Visualization. [PUB]
• Cross-Silo Feature Space Alignment for Federated Learning on Clients with Imbalanced Data. [PUB]
• Federated Unsupervised Domain Generalization Using Global and Local Alignment of Gradients. [PUB]
• In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting. [PUB]
• Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning. [PUB]
• Breaking Data Silos in Parkinson’s Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis. [PUB]
• Federated Unlearning with Gradient Descent and Conflict Mitigation. [PUB]
• Dual-calibrated Co-training Framework for Personalized Federated Semi-Supervised Medical Image Segmentation. [PUB]
• FedSPU: Personalized Federated Learning for Resource-Constrained Devices with Stochastic Parameter Update. [PUB]
• FedSum: Data-Efficient Federated Learning Under Data Scarcity Scenario for Text Summarization. [PUB]
• Data-Free Black-Box Federated Learning via Zeroth-Order Gradient Estimation. [PUB]
• FedCross: Intertemporal Federated Learning Under Evolutionary Games. [PUB]
• Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated Learning. [PUB]
• SemiDFL: A Semi-Supervised Paradigm for Decentralized Federated Learning. [PUB]
• Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach. [PUB]
• Federated Graph-Level Clustering Network. [PUB]
• LiD-FL: Towards List-Decodable Federated Learning. [PUB]
• Convergence Analysis of Federated Learning Methods Using Backward Error Analysis. [PUB]
• Progressive Distribution Matching for Federated Semi-Supervised Learning. [PUB]
• TTA-FedDG: Leveraging Test-Time Adaptation to Address Federated Domain Generalization. [PUB]
• Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach. [PUB]
• EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning. [PUB]
• FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning. [PUB]
• pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning. [PUB]
• FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning. [PUB]
• FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning. [PUB]
• Federated Learning with Heterogeneous LLMs: Integrating Small Student Client Models with a Large Hungry Model. [PUB]
• PA3Fed: Period-Aware Adaptive Aggregation for Improved Federated Learning. [PUB]
• TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning. [PUB]
• FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning. [PUB]
• DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework. [PUB]
• Federated Assemblies. [PUB]
• Federated Causally Invariant Feature Learning. [PUB] [CODE]
• A New Federated Learning Framework Against Gradient Inversion Attacks. [PUB]
• Exploring Vacant Classes in Label-Skewed Federated Learning. [PUB]
• Capture Global Feature Statistics for One-Shot Federated Learning. [PUB]
• Multimodal Fusion Using Multi-View Domains for Data Heterogeneity in Federated Learning. [PUB]
• MFL-Owner: Ownership Protection for Multi-modal Federated Learning via Orthogonal Transform Watermark. [PUB]
• Virtual Nodes Can Help: Tackling Distribution Shifts in Federated Graph Learning. [PUB]
• Beyond Federated Prototype Learning: Learnable Semantic Anchors with Hyperspherical Contrast for Domain-Skewed Data. [PUB]
• Scalable Federated One-Step Multi-View Clustering with Tensorized Regularization. [PUB]
• SADBA: Self-Adaptive Distributed Backdoor Attack Against Federated Learning. [PUB]
• Large Language Models Enhanced Personalized Graph Neural Architecture Search in Federated Learning. [PUB]
• How Does the Smoothness Approximation Method Facilitate Generalization for Federated Adversarial Learning?. [PUB]
• Attribute Inference Attacks for Federated Regression Tasks. [PUB]
• Federated Binary Matrix Factorization Using Proximal Optimization. [PUB]
• Creating Coherence in Federated Non-Negative Matrix Factorization. [PUB]
• Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks. [PUB]
• DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game. [PUB]
• Federated Foundation Models on Heterogeneous Time Series. [PUB]
• FedPop: Federated Population-based Hyperparameter Tuning. [PUB]
• Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy. [PUB]
• EFSkip: A New Error Feedback with Linear Speedup for Compressed Federated Learning with Arbitrary Data Heterogeneity. [PUB]
• Little Is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning. [PUB]
• Breaking Data Silos in Parkinson's Disease Diagnosis: An Adaptive Federated Learning Approach for Privacy-Preserving Facial Expression Analysis. [PUB]
• FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning. [PUB]
• Collaborative Evolution: Multi-Round Learning Between Large and Small Language Models for Emergent Fake News Detection. [PUB]
• Collaborative Learning for 3D Hand-Object Reconstruction and Compositional Action Recognition from Egocentric RGB Videos Using Superquadrics. [PUB]
• DSRC: Learning Density-Insensitive and Semantic-Aware Collaborative Representation Against Corruptions. [PUB]
• Learning to Collaborate with Unknown Agents in the Absence of Reward. [PUB]
• MergeNet: Knowledge Migration Across Heterogeneous Models, Tasks, and Modalities. [PUB]
• Multi-concept Model Immunization through Differentiable Model Merging. [PUB]
• Multi-View Collaborative Learning Network for Speech Deepfake Detection. [PUB]
• Multimodal Promptable Token Merging for Diffusion Models. [PUB]
• Paid with Models: Optimal Contract Design for Collaborative Machine Learning. [PUB]
• The Dynamic Duo of Collaborative Masking and Target for Advanced Masked Autoencoder Learning. [PUB]
• Towards Efficient Collaboration via Graph Modeling in Reinforcement Learning. [PUB]

2024

alt

• Optimal Regret Bounds for Collaborative Learning in Bandits. [PUB]

IJCAI

• Federated Multi-View Clustering via Tensor Factorization. [PUB]
• Efficient Federated Multi-View Clustering with Integrated Matrix Factorization and K-Means. [PUB]
• LG-FGAD: An Effective Federated Graph Anomaly Detection Framework. [PUB]
• Federated Prompt Learning for Weather Foundation Models on Devices. [PUB]
• Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation. [PUB]
• Unlearning during Learning: An Efficient Federated Machine Unlearning Method. [PUB]
• Practical Hybrid Gradient Compression for Federated Learning Systems. [PUB]
• Sample Quality Heterogeneity-aware Federated Causal Discovery through Adaptive Variable Space Selection. [PUB] [CODE]
• Feature Norm Regularized Federated Learning: Utilizing Data Disparities for Model Performance Gains. [PUB] [CODE]
• Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks. [PUB]
• FedConPE: Efficient Federated Conversational Bandits with Heterogeneous Clients. [PUB]
• DarkFed: A Data-Free Backdoor Attack in Federated Learning. [PUB]
• Scalable Federated Unlearning via Isolated and Coded Sharding. [PUB]
• Enhancing Dual-Target Cross-Domain Recommendation with Federated Privacy-Preserving Learning. [PUB]
• Label Leakage in Vertical Federated Learning: A Survey. [PUB]
• The Rise of Federated Intelligence: From Federated Foundation Models Toward Collective Intelligence. [PUB]
• LEAP: Optimization Hierarchical Federated Learning on Non-IID Data with Coalition Formation Game. [PUB]
• EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning. [PUB]
• Knowledge Distillation in Federated Learning: A Practical Guide. [PUB]
• FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization. [PUB]
• FedPFT: Federated Proxy Fine-Tuning of Foundation Models. [PUB] [CODE]
• A Systematic Survey on Federated Semi-supervised Learning. [PUB]
• Intelligent Agents for Auction-based Federated Learning: A Survey. [PUB]
• A Bias-Free Revenue-Maximizing Bidding Strategy for Data Consumers in Auction-based Federated Learning. [PUB]
• Dual Calibration-based Personalised Federated Learning. [PUB]
• Stakeholder-oriented Decision Support for Auction-based Federated Learning. [PUB]
• Redefining Contributions: Shapley-Driven Federated Learning. [PUB] [CODE]
• A Survey on Efficient Federated Learning Methods for Foundation Model Training. [PUB]
• From Optimization to Generalization: Fair Federated Learning against Quality Shift via Inter-Client Sharpness Matching. [PUB] [CODE]
• FBLG: A Local Graph Based Approach for Handling Dual Skewed Non-IID Data in Federated Learning. [PUB]
• FedFa: A Fully Asynchronous Training Paradigm for Federated Learning. [PUB]
• FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning. [PUB]
• FedES: Federated Early-Stopping for Hindering Memorizing Heterogeneous Label Noise. [PUB]
• Personalized Federated Learning for Cross-City Traffic Prediction. [PUB]
• Federated Adaptation for Foundation Model-based Recommendations. [PUB]
• BADFSS: Backdoor Attacks on Federated Self-Supervised Learning. [PUB]
• Estimating before Debiasing: A Bayesian Approach to Detaching Prior Bias in Federated Semi-Supervised Learning. [PUB] [CODE]
• FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning. [PUB]
• Graph Collaborative Expert Finding with Contrastive Learning. [PUB]

AISTATS

• BOBA: Byzantine-Robust Federated Learning with Label Skewness. [PUB] [PDF] [CODE]
• Federated Linear Contextual Bandits with Heterogeneous Clients. [PUB] [PDF] [CODE]
• Federated Experiment Design under Distributed Differential Privacy. [PUB] [PDF] [CODE]
• Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression. [PUB] [PDF]
• Asynchronous SGD on Graphs: a Unified Framework for Asynchronous Decentralized and Federated Optimization. [PUB] [PDF]
• SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization. [PUB] [PDF] [CODE]
• Compression with Exact Error Distribution for Federated Learning. [PUB] [PDF] [CODE]
• Adaptive Federated Minimax Optimization with Lower Complexities. [PUB] [PDF]
• Adaptive Compression in Federated Learning via Side Information. [PUB] [PDF] [CODE]
• On-Demand Federated Learning for Arbitrary Target Class Distributions. [PUB] [CODE]
• FedFisher: Leveraging Fisher Information for One-Shot Federated Learning. [PUB] [PDF] [CODE]
• Queuing dynamics of asynchronous Federated Learning. [PUB] [PDF]
• Personalized Federated X-armed Bandit. [PUB] [PDF] [CODE]
• Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks. [PUB] [CODE]
• Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization. [PUB] [PDF]
• Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters. [PUB] [PDF] [CODE]
• Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domains. [PUB] [PDF] [CODE]
• Analysis of Privacy Leakage in Federated Large Language Models. [PUB] [PDF] [CODE]
• Invariant Aggregator for Defending against Federated Backdoor Attacks. [PUB] [PDF] [CODE]
• Communication-Efficient Federated Learning With Data and Client Heterogeneity. [PUB] [PDF] [CODE]

AAAI

• FedMut: Generalized Federated Learning via Stochastic Mutation. [PUB]
• Federated Partial Label Learning with Local-Adaptive Augmentation and Regularization. [PUB] [PAGE]
• No Prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation. [PUB] [PAGE] [PDF] [CODE]
• Formal Logic Enabled Personalized Federated Learning through Property Inference. [PUB] [PDF]
• Task-Agnostic Privacy-Preserving Representation Learning for Federated Learning against Attribute Inference Attacks. [PUB] [PAGE] [PDF] [CODE]
• FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning. [PUB] [PAGE]
• Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators. [PUB] [PAGE] [PDF]
• Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity. [PUB] [PAGE] [PDF]
• On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning. [PUB]
• FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• Watch Your Head: Assembling Projection Heads to Save the Reliability of Federated Models. [PUB] [PAGE] [PDF]
• FedGCR: Achieving Performance and Fairness for Federated Learning with Distinct Client Types via Group Customization and Reweighting. [PUB] [PAGE] [CODE]
• Federated Modality-Specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation. [PUB] [PAGE] [PDF] [CODE]
• Exploiting Label Skews in Federated Learning with Model Concatenation. [PUB] [PAGE] [PDF] [CODE]
• Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning. [PUB] [PAGE]
• Federated Learning via Input-Output Collaborative Distillation. [PUB] [PAGE] [PDF] [CODE]
• Calibrated One Round Federated Learning with Bayesian Inference in the Predictive Space. [PUB] [PAGE] [PDF] [CODE]
• FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning. [PUB] [PDF] [CODE]
• FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning. [PUB] [PAGE] [PDF]
• FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing. [PUB] [PAGE] [PDF] [CODE]
• Provably Convergent Federated Trilevel Learning. [PUB] [PDF]
• Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts. [PUB] [PAGE]
• General Commerce Intelligence: Glocally Federated NLP-Based Engine for Privacy-Preserving and Sustainable Personalized Services of Multi-Merchants. [PUB] [PAGE]
• EMGAN: Early-Mix-GAN on Extracting Server-Side Model in Split Federated Learning. [PUB] [PAGE] [CODE]
• FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels. [PUB] [PAGE] [PDF] [CODE]
• Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images. [PUB] [PAGE] [PDF] [CODE]
• FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning. [PUB] [PDF] [CODE]
• Federated X-armed Bandit. [PUB] [PAGE] [PDF] [CODE]
• Algorithmic Foundation of Federated Learning with Sequential Data. [PUB]
• UFDA: Universal Federated Domain Adaptation with Practical Assumptions. [PUB] [PAGE] [PDF] [CODE]
• FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update. [PUB] [PAGE] [PDF]
• Language-Guided Transformer for Federated Multi-Label Classification. [PUB] [PAGE] [PDF] [CODE]
• FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers. [PUB] [PAGE] [CODE]
• Beyond Traditional Threats: A Persistent Backdoor Attack on Federated Learning. [PUB] [PAGE] [CODE]
• Federated Learning with Extremely Noisy Clients via Negative Distillation. [PUB] [PAGE] [PDF] [CODE]
• FedST: Federated Style Transfer Learning for Non-IID Image Segmentation. [PUB] [PAGE] [学报] [CODE]
• PPIDSG: A Privacy-Preserving Image Distribution Sharing Scheme with GAN in Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• A Privacy Preserving Federated Learning (PPFL) Based Cognitive Digital Twin (CDT) Framework for Smart Cities. [PUB]
• A Primal-Dual Algorithm for Hybrid Federated Learning. [PUB] [PAGE] [PDF]
• FedLF: Layer-Wise Fair Federated Learning. [PUB] [PAGE] [CODE]
• Towards Fair Graph Federated Learning via Incentive Mechanisms. [PUB] [PAGE] [PDF] [CODE]
• Towards the Robustness of Differentially Private Federated Learning. [PUB] [PAGE]
• Resisting Backdoor Attacks in Federated Learning via Bidirectional Elections and Individual Perspective. [PUB] [PAGE] [PDF] [CODE]
• Integer Is Enough: When Vertical Federated Learning Meets Rounding. [PUB] [PAGE]
• CLIP-Guided Federated Learning on Heterogeneity and Long-Tailed Data. [PUB] [PAGE] [PDF] [CODE]
• Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning. [PUB] [PAGE] [PDF] [CODE]
• Multi-Dimensional Fair Federated Learning. [PUB] [PAGE] [PDF]
• HiFi-Gas: Hierarchical Federated Learning Incentive Mechanism Enhanced Gas Usage Estimation. [PUB]
• On the Role of Server Momentum in Federated Learning. [PUB] [PDF]
• FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants. [PUB] [PAGE] [PDF]
• z-SignFedAvg: A Unified Stochastic Sign-Based Compression for Federated Learning. [PUB] [PAGE] [PDF]
• Data Disparity and Temporal Unavailability Aware Asynchronous Federated Learning for Predictive Maintenance on Transportation Fleets. [PUB] [PAGE]
• Federated Graph Learning under Domain Shift with Generalizable Prototypes. [PUB] [PAGE]
• TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients. [PUB] [PAGE] [PDF] [CODE]
• Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization. [PUB] [PDF] [CODE]
• Concealing Sensitive Samples against Gradient Leakage in Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise. [PUB] [PAGE] [PDF] [CODE]
• Federated Causality Learning with Explainable Adaptive Optimization. [PUB] [PAGE] [PDF]
• Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users. [PUB] [PAGE] [PDF]
• Exploring One-Shot Semi-supervised Federated Learning with Pre-trained Diffusion Models. [PUB] [PDF]
• Diversity-Authenticity Co-constrained Stylization for Federated Domain Generalization in Person Re-identification. [PUB] [PAGE]
• PerFedRLNAS: One-for-All Personalized Federated Neural Architecture Search. [PUB] [PAGE]
• Efficient Asynchronous Federated Learning with Prospective Momentum Aggregation and Fine-Grained Correction. [PUB] [PAGE]
• Adversarial Attacks on Federated-Learned Adaptive Bitrate Algorithms. [PUB]
• FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning. [PUB] [PAGE] [PDF] [CODE]
• LR-XFL: Logical Reasoning-Based Explainable Federated Learning. [PUB] [PDF] [CODE]
• A Huber Loss Minimization Approach to Byzantine Robust Federated Learning. [PUB] [PAGE] [PDF]
• Knowledge-Aware Parameter Coaching for Personalized Federated Learning. [PUB] [PAGE]
• Federated Label-Noise Learning with Local Diversity Product Regularization. [PUB] [PAGE] [SUPP]
• Adapted Weighted Aggregation in Federated Learning (Student Abstract). [PUB]
• Knowledge Transfer via Compact Model in Federated Learning (Student Abstract). [PUB] [PAGE]
• PICSR: Prototype-Informed Cross-Silo Router for Federated Learning (Student Abstract). [PUB] [PAGE]
• Adapted Weighted Aggregation in Federated Learning. [PUB]
• Collaborative Consortium of Foundation Models for Open-World Few-Shot Learning. [PUB] [CODE]
• Collaborative Learning across Heterogeneous Systems with Pre-Trained Models. [PUB]
• Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis. [PUB]
• Communication Efficient Distributed Newton Method over Unreliable Networks. [PUB]
• DI-V2X: Learning Domain-Invariant Representation for Vehicle-Infrastructure Collaborative 3D Object Detection. [PUB] [CODE]
• Foreseeing Reconstruction Quality of Gradient Inversion: An Optimization Perspective. [PUB]
• Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing. [PUB]
• High-Fidelity Gradient Inversion in Distributed Learning. [PUB] [CODE]
• Learn How to See: Collaborative Embodied Learning for Object Detection and Camera Adjusting. [PUB] [CODE]

2023

AI

• Privacy-preserving graph convolution network for federated item recommendation. [PUB]
• Transfer learning for collaborative recommendation with biased and unbiased data. [PUB]

AAAI

• Win-Win: A Privacy-Preserving Federated Framework for Dual-Target Cross-Domain Recommendation. [PUB]
• Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense. [PUB] [PDF] [CODE]
• Incentive-Boosted Federated Crowdsourcing. [PUB] [PDF]
• Tackling Data Heterogeneity in Federated Learning with Class Prototypes. [PUB] [PDF] [CODE]
• FairFed: Enabling Group Fairness in Federated Learning. [PUB] [PDF] [解读]
• Federated Robustness Propagation: Sharing Adversarial Robustness in Heterogeneous Federated Learning. [PUB] [CODE]
• Complement Sparsification: Low-Overhead Model Pruning for Federated Learning. [PUB]
• Almost Cost-Free Communication in Federated Best Arm Identification. [PUB] [PDF]
• Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning. [PUB] [PDF]
• Poisoning with Cerberus: Stealthy and Colluded Backdoor Attack against Federated Learning. [PUB]
• FedMDFG: Federated Learning with Multi-Gradient Descent and Fair Guidance. [PUB] [CODE]
• Securing Secure Aggregation: Mitigating Multi-Round Privacy Leakage in Federated Learning. [PUB] [PDF] [VIDEO] [CODE]
• Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [PUB] [PDF] [CODE]
• Efficient Distribution Similarity Identification in Clustered Federated Learning via Principal Angles between Client Data Subspaces. [PUB] [PDF] [CODE]
• FedABC: Targeting Fair Competition in Personalized Federated Learning. [PUB] [PDF]
• Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework. [PUB] [PDF]
• FedGS: Federated Graph-Based Sampling with Arbitrary Client Availability. [PUB] [PDF] [CODE]
• Faster Adaptive Federated Learning. [PUB] [PDF]
• FedNP: Towards Non-IID Federated Learning via Federated Neural Propagation. [PUB] [CODE] [VIDEO] [SUPP]
• Bayesian Federated Neural Matching That Completes Full Information. [PUB] [PDF]
• CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax Problems. [PUB] [PDF] [CODE]
• Federated Generative Model on Multi-Source Heterogeneous Data in IoT. [PUB]
• DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness. [PUB]
• FedALA: Adaptive Local Aggregation for Personalized Federated Learning. [PUB] [PDF] [CODE]
• Delving into the Adversarial Robustness of Federated Learning. [PUB] [PDF]
• On the Vulnerability of Backdoor Defenses for Federated Learning. [PUB] [PDF] [CODE]
• Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model. [PUB] [PDF]
• Federated Learning on Non-IID Graphs via Structural Knowledge Sharing. [PDF] [CODE]
• FedGS: Federated Graph-based Sampling with Arbitrary Client Availability. [PDF] [CODE]
• Incentive-boosted Federated Crowdsourcing. [PDF]
• DPAUC: Differentially Private AUC Computation in Federated Learning. [PUB] [CODE]
• Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. [PUB]
• Industry-Scale Orchestrated Federated Learning for Drug Discovery. [PUB]
• A Crowd-AI Collaborative Duo Relational Graph Learning Framework towards Social Impact Aware Photo Classification. [PUB]
• CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning. [PUB]
• Heterogeneous-Branch Collaborative Learning for Dialogue Generation. [PUB]
• A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract). [PUB]
• Clustered Federated Learning for Heterogeneous Data (Student Abstract). [PUB]
• MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract). [PUB]

AAAI Special Tracks

• DPAUC: Differentially Private AUC Computation in Federated Learning. [PUB] [PDF] [CODE]

AAAI Special Programs

• Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout. [PUB] [PDF]
• Industry-Scale Orchestrated Federated Learning for Drug Discovery. [PUB] [PDF] [VIDEO]
• A Federated Learning Monitoring Tool for Self-Driving Car Simulation (Student Abstract). [PUB]
• MGIA: Mutual Gradient Inversion Attack in Multi-Modal Federated Learning (Student Abstract). [PUB]
• Clustered Federated Learning for Heterogeneous Data (Student Abstract). [PUB]

IJCAI

• FedSampling: A Better Sampling Strategy for Federated Learning. [PUB] [PDF] [CODE]
• HyperFed: Hyperbolic Prototypes Exploration with Consistent Aggregation for Non-IID Data in Federated Learning. [PUB] [PDF]
• FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning. [PUB] [PDF] [CODE]
• Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation. [PUB]
• Federated Graph Semantic and Structural Learning. [PUB]
• BARA: Efficient Incentive Mechanism with Online Reward Budget Allocation in Cross-Silo Federated Learning. [PUB] [PDF]
• FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment. [PUB] [PDF]
• FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation. [PUB] [PDF]
• Globally Consistent Federated Graph Autoencoder for Non-IID Graphs. [PUB] [CODE]
• Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning. [PUB]
• Dual Personalization on Federated Recommendation. [PUB] [PDF] [CODE]
• FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity. [PUB] [PDF] [CODE]
• Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning. [PUB] [PDF] [CODE]
• FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks. [PUB] [PDF] [CODE]
• FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer. [PUB] [PDF]
• Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data. [PUB] [PDF] [CODE]
• FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training. [PUB] [CODE]
• A Survey of Federated Evaluation in Federated Learning. [PUB]
• Learn and Sample Together: Collaborative Generation for Graphic Design Layout. [PUB]
• Prompt Learns Prompt: Exploring Knowledge-Aware Generative Prompt Collaboration For Video Captioning. [PUB]
• SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract). [PUB]
• Q-Learning-Based Model Predictive Variable Impedance Control for Physical Human-Robot Collaboration (Extended Abstract). [PUB]

IJCAI Survey Track

• Bayesian Federated Learning: A Survey. [PDF]
• A Survey of Federated Evaluation in Federated Learning. [PUB] [PDF]

IJCAI Journal Track

• SAMBA: A Generic Framework for Secure Federated Multi-Armed Bandits (Extended Abstract). [PUB]

AISTATS

• The communication cost of security and privacy in federated frequency estimation. [PUB] [CODE]
• Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout. [PUB] [CODE]
• Federated Learning under Distributed Concept Drift. [PUB] [CODE]
• Characterizing Internal Evasion Attacks in Federated Learning. [PUB] [CODE]
• Federated Asymptotics: a model to compare federated learning algorithms. [PUB] [CODE]
• Private Non-Convex Federated Learning Without a Trusted Server. [PUB] [CODE]
• Federated Learning for Data Streams. [PUB] [CODE]
• Nothing but Regrets — Privacy-Preserving Federated Causal Discovery. [PUB] [CODE]
• Active Membership Inference Attack under Local Differential Privacy in Federated Learning. [PUB] [CODE]
• Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms. [PUB]
• Byzantine-Robust Federated Learning with Optimal Statistical Rates. [PUB] [CODE]
• Dropout-Resilient Secure Multi-Party Collaborative Learning with Linear Communication Complexity. [PUB]
• FAIR: Fair Collaborative Active Learning with Individual Rationality for Scientific Discovery. [PUB]

2022

ai

• Q-Learning-based model predictive variable impedance control for physical human-robot collaboration. [PUB]

AISTATS

• Towards Understanding Biased Client Selection in Federated Learning. [PUB] [CODE]
• FLIX: A Simple and Communication-Efficient Alternative to Local Methods in Federated Learning. [PUB] [PDF] [CODE]
• Sharp Bounds for Federated Averaging (Local SGD) and Continuous Perspective. [PUB] [PDF] [CODE]
• Federated Reinforcement Learning with Environment Heterogeneity. [PUB] [PDF] [CODE]
• Federated Myopic Community Detection with One-shot Communication. [PUB] [PDF]
• Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits. [PUB] [PDF] [CODE]
• Towards Federated Bayesian Network Structure Learning with Continuous Optimization. [PUB] [PDF] [CODE]
• Federated Learning with Buffered Asynchronous Aggregation. [PUB] [PDF] [VIDEO]
• Differentially Private Federated Learning on Heterogeneous Data. [PUB] [PDF] [CODE]
• SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification. [PUB] [PDF] [CODE] [VIDEO]
• Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning. [PUB] [PDF]
• Federated Functional Gradient Boosting. [PUB] [PDF] [CODE]
• QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning. [PUB] [PDF] [CODE] [VIDEO]
• Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time. [PUB]

IJCAI

• Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting kg.. [PUB] [PDF] [CODE]
• Personalized Federated Learning With a Graph. [PUB] [PDF] [CODE]
• Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification. [PUB] [PDF]
• Adapt to Adaptation: Learning Personalization for Cross-Silo Federated Learning. [PUB] [PDF] [CODE]
• Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning. [PUB] [PDF]
• Private Semi-Supervised Federated Learning. [PUB]
• Continual Federated Learning Based on Knowledge Distillation. [PUB]
• Federated Learning on Heterogeneous and Long-Tailed Data via Classifier Re-Training with Federated Features. [PUB] [PDF] [CODE]
• Federated Multi-Task Attention for Cross-Individual Human Activity Recognition. [PUB]
• Personalized Federated Learning with Contextualized Generalization. [PUB] [PDF]
• Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection. [PUB] [PDF]
• FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning. [PUB] [PDF] [CODE]
• FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server. [PUB] [PDF]
• Towards Verifiable Federated Learning surv.. [PUB] [PDF]
• Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting. [PUB]
• Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios. [PUB]
• Towards Verifiable Federated Learning. [PUB]
• A Survey on Gradient Inversion: Attacks, Defenses and Future Directions. [PUB]

AAAI

• HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images. [PUB] [PDF] [CODE] [解读]
• Federated Learning for Face Recognition with Gradient Correction. [PUB] [PDF]
• SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data. [PUB] [PDF] [CODE] [解读]
• SmartIdx: Reducing Communication Cost in Federated Learning by Exploiting the CNNs Structures. [PUB] [CODE]
• Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network. [PUB] [PDF]
• Seizing Critical Learning Periods in Federated Learning. [PUB] [PDF]
• Coordinating Momenta for Cross-silo Federated Learning. [PUB] [PDF]
• FedProto: Federated Prototype Learning over Heterogeneous Devices. [PUB] [PDF] [CODE]
• FedSoft: Soft Clustered Federated Learning with Proximal Local Updating. [PUB] [PDF] [CODE]
• Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better. [PUB] [PDF] [CODE]
• FedFR: Joint Optimization Federated Framework for Generic and Personalized Face Recognition. [PUB] [PDF] [CODE]
• SplitFed: When Federated Learning Meets Split Learning. [PUB] [PDF] [CODE]
• Efficient Device Scheduling with Multi-Job Federated Learning. [PUB] [PDF]
• Implicit Gradient Alignment in Distributed and Federated Learning. [PUB] [PDF]
• Federated Nearest Neighbor Classification with a Colony of Fruit-Flies. [PUB] [PDF] [CODE]
• A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. [PUB]
• Contribution-Aware Federated Learning for Smart Healthcare. [PUB]
• Cross-Modal Federated Human Activity Recognition via Modality-Agnostic and Modality-Specific Representation Learning. [PUB]
• CrowdFL: A Marketplace for Crowdsourced Federated Learning. [PUB]
• FedInv: Byzantine-Robust Federated Learning by Inversing Local Model Updates. [PUB]
• FedProto: Federated Prototype Learning across Heterogeneous Clients. [PUB]
• Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning. [PUB]
• Preserving Privacy in Federated Learning with Ensemble Cross-Domain Knowledge Distillation. [PUB]
• CPRAL: Collaborative Panoptic-Regional Active Learning for Semantic Segmentation. [PUB]
• Cross-Dataset Collaborative Learning for Semantic Segmentation in Autonomous Driving. [PUB]
• Demystifying Why Local Aggregation Helps: Convergence Analysis of Hierarchical SGD. [PUB]
• Incentivizing Collaboration in Machine Learning via Synthetic Data Rewards. [PUB]
• Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration. [PUB]
• AsyncFL: Asynchronous Federated Learning Using Majority Voting with Quantized Model Updates (Student Abstract). [PUB]
• Class-Wise Adaptive Self Distillation for Federated Learning on Non-IID Data (Student Abstract). [PUB]
• FedCC: Federated Learning with Consensus Confirmation for Byzantine Attack Resistance (Student Abstract). [PUB]

ALT

• Iterated Vector Fields and Conservatism, with Applications to Federated Learning. [PUB] [PDF]

2021

IJCAI

• Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization. [PUB] [PDF] [VIDEO]
• Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning. [PUB] [PDF]
• FedSpeech: Federated Text-to-Speech with Continual Learning. [PUB] [PDF]
• Practical One-Shot Federated Learning for Cross-Silo Setting. [PUB] [PDF] [CODE]
• Federated Model Distillation with Noise-Free Differential Privacy. [PUB] [PDF] [VIDEO]
• LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy. [PUB] [PDF]
• Federated Learning with Fair Averaging. :fire:. [PUB] [PDF] [CODE]
• H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning. [PUB] [PDF]
• Communication-efficient and Scalable Decentralized Federated Edge Learning. [PUB]
• Federated Learning with Fair Averaging. [PUB] [CODE]
• Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare. [PUB]
• Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification. [PUB] [CODE]

AAAI

• Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating. [PUB] [PDF] [VIDEO]
• FedRec++: Lossless Federated Recommendation with Explicit Feedback. [PUB] [VIDEO]
• Federated Multi-Armed Bandits. [PUB] [PDF] [CODE] [VIDEO]
• On the Convergence of Communication-Efficient Local SGD for Federated Learning. [PUB] [VIDEO]
• FLAME: Differentially Private Federated Learning in the Shuffle Model. [PUB] [PDF] [VIDEO] [CODE]
• Toward Understanding the Influence of Individual Clients in Federated Learning. [PUB] [PDF] [VIDEO]
• Provably Secure Federated Learning against Malicious Clients. [PUB] [PDF] [VIDEO] [SLIDE]
• Personalized Cross-Silo Federated Learning on Non-IID Data. [PUB] [PDF] [VIDEO] [UC.]
• Model-Sharing Games: Analyzing Federated Learning under Voluntary Participation. [PUB] [PDF] [CODE] [VIDEO]
• Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning. [PUB] [PDF] [VIDEO]
• Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models. [PUB] [PDF] [VIDEO] [CODE]
• Addressing Class Imbalance in Federated Learning. [PUB] [PDF] [VIDEO] [CODE] [解读]
• Defending against Backdoors in Federated Learning with Robust Learning Rate. [PUB] [PDF] [VIDEO] [CODE]
• AI-Infused Collaborative Inquiry in Upper Elementary School: A Game-Based Learning Approach. [PUB]
• Collaborative Group Learning. [PUB]
• Communication-Aware Collaborative Learning. [PUB]
• Communication-Efficient Frank-Wolfe Algorithm for Nonconvex Decentralized Distributed Learning. [PUB]
• DeepCollaboration: Collaborative Generative and Discriminative Models for Class Incremental Learning. [PUB]
• Differentially Private and Communication Efficient Collaborative Learning. [PUB]
• Peer Collaborative Learning for Online Knowledge Distillation. [PUB]
• STL-SGD: Speeding Up Local SGD with Stagewise Communication Period. [PUB]
• A Serverless Approach to Federated Learning Infrastructure Oriented for IoT/Edge Data Sources (Student Abstract). [PUB]

AISTATS

• Free-rider Attacks on Model Aggregation in Federated Learning. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Federated f-differential privacy. [PUB] [CODE] [VIDEO] [SUPP]
• Federated learning with compression: Unified analysis and sharp guarantees :fire:. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Shuffled Model of Differential Privacy in Federated Learning. [PUB] [VIDEO] [SUPP]
• Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning. [PUB] [PDF] [VIDEO] [SUPP]
• Federated Multi-armed Bandits with Personalization. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Towards Flexible Device Participation in Federated Learning. [PUB] [PDF] [VIDEO] [SUPP]
• Federated Learning with Compression: Unified Analysis and Sharp Guarantees. [PUB]
• Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. [PUB]
• LENA: Communication-Efficient Distributed Learning with Self-Triggered Gradient Uploads. [PUB]
• Local SGD: Unified Theory and New Efficient Methods. [PUB]
• One-Round Communication Efficient Distributed M-Estimation. [PUB]

2020

IJCAI

• Federated Meta-Learning for Fraudulent Credit Card Detection. [PUB] [VIDEO]
• A Multi-player Game for Studying Federated Learning Incentive Schemes. [PUB] [CODE] [解读]
• A De Novo Divide-and-Merge Paradigm for Acoustic Model Optimization in Automatic Speech Recognition. [PUB]
• CDC: Classification Driven Compression for Bandwidth Efficient Edge-Cloud Collaborative Deep Learning. [PUB]
• Collaborative Learning of Depth Estimation, Visual Odometry and Camera Relocalization from Monocular Videos. [PUB]

AAAI

• Practical Federated Gradient Boosting Decision Trees. [PUB] [PDF] [CODE]
• Federated Learning for Vision-and-Language Grounding Problems. [PUB]
• Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework. [PUB]
• Federated Patient Hashing. [PUB]
• Robust Federated Learning via Collaborative Machine Teaching. [PUB] [PDF]
• FedVision: An Online Visual Object Detection Platform Powered by Federated Learning. [PUB] [PDF] [CODE]
• Auto-GAN: Self-Supervised Collaborative Learning for Medical Image Synthesis. [PUB]
• Collaborative Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning. [PUB]
• Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning. [PUB]

AISTATS

• FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization. [PUB] [PDF] [VIDEO] [SUPP]
• How To Backdoor Federated Learning :fire:. [PUB] [PDF] [VIDEO] [CODE] [SUPP]
• Federated Heavy Hitters Discovery with Differential Privacy. [PUB] [PDF] [VIDEO] [SUPP]
• How To Backdoor Federated Learning. [PUB]
• Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction. [PUB]
• Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs. [PUB]
• Tighter Theory for Local SGD on Identical and Heterogeneous Data. [PUB]

2019

aaai

• Collaboration Based Multi-Label Learning. [PUB]
• Improving Domain-Specific Classification by Collaborative Learning with Adaptation Networks. [PUB]
• Multi-View Multi-Instance Multi-Label Learning Based on Collaborative Matrix Factorization. [PUB]

aistats

• Exploring Fast and Communication-Efficient Algorithms in Large-Scale Distributed Networks. [PUB]
• Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication. [PUB]

IJCAI

• Multi-Agent Visualization for Explaining Federated Learning. [PUB] [VIDEO]
• A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification. [PUB]
• Collaborative Metric Learning with Memory Network for Multi-Relational Recommender Systems. [PUB]
• Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks. [PUB]
• Feature Evolution Based Multi-Task Learning for Collaborative Filtering with Social Trust. [PUB]
• Learning Swarm Behaviors using Grammatical Evolution and Behavior Trees. [PUB]

2018

aaai

• Robust Collaborative Discriminative Learning for RGB-Infrared Tracking. [PUB]
• Uplink Communication Efficient Differentially Private Sparse Optimization With Feature-Wise Distributed Data. [PUB]

ijcai

• Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection. [PUB]
• Collaborative and Attentive Learning for Personalized Image Aesthetic Assessment. [PUB]
• Collaborative Learning for Weakly Supervised Object Detection. [PUB]
• CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering. [PUB]
• Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach. [PUB]
• Learning and Communicating the Latent States of Human-Machine Collaboration. [PUB]

2017

aistats

• Communication-efficient Distributed Sparse Linear Discriminant Analysis. [PUB]
• Decentralized Collaborative Learning of Personalized Models over Networks. [PUB]

2016

ai

• H-index manipulation by merging articles: Models, theory, and experiments. [PUB]

aistats

• Communication Efficient Distributed Agnostic Boosting. [PUB]

ijcai

• Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction. [PUB]
• Modeling Contagious Merger and Acquisition via Point Processes with a Profile Regression Prior. [PUB]

2015

ijcai

• H-Index Manipulation by Merging Articles: Models, Theory, and Experiments. [PUB]

2014

aistats

• Scalable Collaborative Bayesian Preference Learning. [PUB] [CODE]

2013

aaai

• Learning Collaborative Impedance-Based Robot Behaviors. [PUB]

ai

• Transfer learning in heterogeneous collaborative filtering domains. [PUB]

2012

aaai

• Transfer Learning in Collaborative Filtering with Uncertain Ratings. [PUB]

ai

• Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. [PUB]

2011

aaai

• Mechanism Design for Federated Sponsored Search Auctions. [PUB]

2010

aaai

• Transfer Learning in Collaborative Filtering for Sparsity Reduction. [PUB]

2007

aaai

• A Model-based Approach for Merging Prioritized Knowledge Bases in Possibilistic Logic. [PUB]
• PLOW: A Collaborative Task Learning Agent. [PUB]

2000

alt

• Extracting Information from the Web for Concept Learning and Collaborative Filtering. [PUB]

2026

jmlr

• Communication-efficient Distributed Statistical Inference for Massive Data with Heterogeneous Auxiliary Information. [PUB]

Machine Learning

• FDGReID: Federated Domain Generalization for Person Re-identification. [PUB]
• FedBNR: A Fully Global Federated Gaussian Process. [PUB]
• Federated Learning on Riemannian Manifolds with Differential Privacy. [PUB]
• Federated SHAP: Privacy-Preserving and Consistent Post-hoc Explainability in Federated Learning. [PUB]
• FedGES: A Federated Learning Approach for Bayesian Network Structure Learning. [PUB]
• Collaborative Multivariate Time Series Forecasting via Variable-Tailored Inter-temporal Graph and Adaptive-Smooth Frequency Fusion. [PUB]
• PC-MoE: memory-efficient and privacy-preserving collaborative training for Mixture-of-Experts LLMs. [PUB]

TPAMI

• A Bayesian Framework for Clustered Federated Learning. [PUB]
• Adaptive Batch Size Time Evolving Stochastic Gradient Descent for Federated Learning. [PUB]
• Communication-Efficient Federated Multi-View Clustering. [PUB]
• Decentralized Federated Learning With Distributed Aggregation Weight Optimization. [PUB]
• Exploring the Vulnerabilities of Federated Learning: A Deep Dive Into Gradient Inversion Attacks. [PUB]
• FedFask: Fast Sketching Distributed PCA for Large-Scale Federated Data. [PUB]
• Sample-Level Prototypical Federated Learning. [PUB]
• Slack Federated Adversarial Training. [PUB]
• Toward Understanding Generalization and Stability Gaps Between Centralized and Decentralized Federated Learning. [PUB]
• Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging. [PUB]

2025

JMLR

• Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback. [PUB]
• Client Selection for Federated Policy Optimization with Environment Heterogeneity. [PUB]
• FedHB: Hierarchical Bayesian Federated Learning. [PUB]
• PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark. [PUB]
• Sharp Bounds for Sequential Federated Learning on Heterogeneous Data. [PUB]
• Collaborative likelihood-ratio estimation over graphs. [PUB]

machine learning

• Auction-based incentive mechanism with personalized privacy protection in federated learning. [PUB]
• DP-FedSecure: a secure and efficient federated learning scheme based on adaptive differential privacy. [PUB]
• Efficient federated unlearning under plausible deniability. [PUB] [CODE]
• Federated causal inference from observational data. [PUB]
• Fedflow: a personalized federated learning framework for passenger flow prediction. [PUB]
• FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learning. [PUB]
• HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning. [PUB]
• Improve global generalization for personalized federated learning within a Stackelberg game. [PUB]
• TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. [PUB]
• Adaptive collaborative minority oversampling for multi-class imbalanced classification. [PUB]
• Multi-modal co-learning for Earth observation: enhancing single-modality models via modality collaboration. [PUB]

UAI

• Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication. [PUB]
• FALCON: Adaptive Cross-Domain APT Attack Investigation with Federated Causal Learning. [PUB]
• FeDCM: Federated Learning of Deep Causal Generative Models. [PUB]
• Federated Rényi Fair Inference in Federated Heterogeneous System. [PUB]
• FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning. [PUB]
• ELF: Federated Langevin Algorithms with Primal, Dual and Bidirectional Compression. [PUB]
• FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set. [PUB]
• Cutting Through Privacy: A Hyperplane-Based Data Reconstruction Attack in Federated Learning. [PUB]
• Conformal Prediction for Federated Graph Neural Networks with Missing Neighbor Information. [PUB]
• Hindsight Merging: Diverse Data Generation with Language Models. [PUB]

ICML

• Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off. [PUB] [CODE]
• Less is More: Federated Graph Learning with Alleviating Topology Heterogeneity from A Causal Perspective. [PUB]
• SecEmb: Sparsity-Aware Secure Federated Learning of On-Device Recommender System with Large Embedding. [PUB] [CODE]
• Causality Inspired Federated Learning for OOD Generalization. [PUB] [CODE]
• Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging. [PUB] [CODE]
• One-Shot Heterogeneous Federated Learning with Local Model-Guided Diffusion Models. [PUB] [CODE]
• FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models. [PUB]
• An Effective and Secure Federated Multi-View Clustering Method with Information-Theoretic Perspective. [PUB] [CODE]
• Gap-Dependent Bounds for Federated $Q$-Learning. [PUB]
• FedBEns: One-Shot Federated Learning based on Bayesian Ensemble. [PUB] [CODE]
• NTK-DFL: Enhancing Decentralized Federated Learning in Heterogeneous Settings via Neural Tangent Kernel. [PUB] [CODE]
• Federated Learning for Feature Generalization with Convex Constraints. [PUB] [CODE]
• Uncertainty-Based Extensible Codebook for Discrete Federated Learning in Heterogeneous Data Silos. [PUB] [CODE]
• Towards Trustworthy Federated Learning with Untrusted Participants. [PUB]
• Multi-Session Budget Optimization for Forward Auction-based Federated Learning. [PUB]
• Federated Disentangled Tuning with Textual Prior Decoupling and Visual Dynamic Adaptation. [PUB] [CODE]
• LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation. [PUB]
• Momentum-Driven Adaptivity: Towards Tuning-Free Asynchronous Federated Learning. [PUB]
• Differentially Private Federated $k$-Means Clustering with Server-Side Data. [PUB] [CODE]
• CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning. [PUB]
• Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees. [PUB] [CODE]
• $S^2$FGL: Spatial Spectral Federated Graph Learning. [PUB] [CODE]
• FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation. [PUB] [CODE]
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning. [PUB] [CODE]
• Efficient Heterogeneity-Aware Federated Active Data Selection. [PUB]
• Splitting with Importance-aware Updating for Heterogeneous Federated Learning with Large Language Models. [PUB] [CODE]
• Rethinking the Temperature for Federated Heterogeneous Distillation. [PUB]
• FedClean: A General Robust Label Noise Correction for Federated Learning. [PUB]
• Federated Causal Structure Learning with Non-identical Variable Sets. [PUB]
• FedECADO: A Dynamical System Model of Federated Learning. [PUB]
• Efficient Federated Incomplete Multi-View Clustering. [PUB] [CODE]
• Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance. [PUB] [CODE]
• Local Pan-privacy for Federated Analytics. [PUB]
• FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning. [PUB] [CODE]
• Hybrid Batch Normalisation: Resolving the Dilemma of Batch Normalisation in Federated Learning. [PUB] [CODE]
• Private Federated Learning using Preference-Optimized Synthetic Data. [PUB] [CODE]
• Enhancing Foundation Models with Federated Domain Knowledge Infusion. [PUB]
• FedPHA: Federated Prompt Learning for Heterogeneous Client Adaptation. [PUB] [CODE]
• Federated Oriented Learning: A Practical One-Shot Personalized Federated Learning Framework. [PUB] [CODE]
• Federated Node-Level Clustering Network with Cross-Subgraph Link Mending. [PUB]
• Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [PUB] [CODE]
• FedSSI: Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence. [PUB]
• Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework. [PUB] [CODE]
• DTZO: Distributed Trilevel Zeroth Order Learning with Provable Non-Asymptotic Convergence. [PUB]
• On-Device Collaborative Language Modeling via a Mixture of Generalists and Specialists. [PUB]
• Safe-EF: Error Feedback for Non-smooth Constrained Optimization. [PUB] [CODE]
• Gradient Inversion of Multimodal Models. [PUB] [CODE]
• Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg. [PUB] [CODE]
• Decoupled SGDA for Games with Intermittent Strategy Communication. [PUB]
• Private Model Personalization Revisited. [PUB]
• Leveraging Randomness in Model and Data Partitioning for Privacy Amplification. [PUB]
• Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up. [PUB] [CODE]
• Voronoi-grid-based Pareto Front Learning and Its Application to Collaborative Federated Learning. [PUB] [CODE]
• FedSMU: Communication-Efficient and Generalization-Enhanced Federated Learning through Symbolic Model Updates. [PUB] [CODE]
• One Arrow, Two Hawks: Sharpness-aware Minimization for Federated Learning via Global Model Trajectory. [PUB] [CODE]
• Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shifts. [PUB]
• Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning. [PUB] [CODE]
• GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention. [PUB] [CODE]
• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing. [PUB]
• BSemiFL: Semi-supervised Federated Learning via a Bayesian Approach. [PUB]
• Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning. [PUB]
• EAGLES: Towards Effective, Efficient, and Economical Federated Graph Learning via Unified Sparsification. [PUB] [CODE]
• Harnessing Heterogeneous Statistical Strength for Personalized Federated Learning via Hierarchical Bayesian Inference. [PUB] [CODE]
• Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models. [PUB] [CODE]
• Generalization in Federated Learning: A Conditional Mutual Information Framework. [PUB]
• The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning. [PUB] [CODE]
• Improved Coresets for Vertical Federated Learning: Regularized Linear and Logistic Regressions. [PUB] [CODE]
• Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation. [PUB]
• Federated In-Context Learning: Iterative Refinement for Improved Answer Quality. [PUB]
• SPMC: Self-Purifying Federated Backdoor Defense via Margin Contribution. [PUB] [CODE]
• You Get What You Give: Reciprocally Fair Federated Learning. [PUB]
• Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead. [PUB] [CODE]
• Byzantine-Resilient Federated Alternating Gradient Descent and Minimization for Partly-Decoupled Low Rank Matrix Learning. [PUB]
• Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts. [PUB]
• Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks. [PUB]
• BECAME: Bayesian Continual Learning with Adaptive Model Merging. [PUB]
• BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly. [PUB]
• Bring Reason to Vision: Understanding Perception and Reasoning through Model Merging. [PUB]
• CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging. [PUB]
• CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging. [PUB]
• Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold. [PUB]
• Efficient Time Series Processing for Transformers and State-Space Models through Token Merging. [PUB]
• HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training. [PUB]
• Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent. [PUB]
• Mutual Learning for SAM Adaptation: A Dual Collaborative Network Framework for Source-Free Domain Transfer. [PUB]
• No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces. [PUB] [CODE]
• Pareto Merging: Multi-Objective Optimization for Preference-Aware Model Merging. [PUB]
• Representation Surgery in Model Merging with Probabilistic Modeling. [PUB]
• Scalable Model Merging with Progressive Layer-wise Distillation. [PUB]
• ToMA: Token Merge with Attention for Diffusion Models. [PUB] [CODE]
• Whoever Started the interference Should End It: Guiding Data-Free Model Merging via Task Vectors. [PUB]

Mach Learn

• HFIA: a parasitic feature inference attack and gradient-based defense strategy in SplitNN-based vertical federated learning. [PUB]

• Fedflow: a personalized federated learning framework for passenger flow prediction. [PUB]

• Federated causal inference from observational data. [PUB]

• TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. [PUB]

• Improve global generalization for personalized federated learning within a Stackelberg game. [PUB]

• Efficient federated unlearning under plausible deniability. [PUB] [CODE]

• Auction-based incentive mechanism with personalized privacy protection in federated learning. [PUB]

• DP-FedSecure: a secure and efficient federated learning scheme based on adaptive differential privacy. [PUB]

• FediOS: decoupling orthogonal subspaces for personalization in feature-skew federated learning. [PUB]

ICLR

• Energy-based Backdoor Defense Against Federated Graph Learning. [PUB]
• DEPT: Decoupled Embeddings for Pre-training Language Models. [PUB]
• Subgraph Federated Learning for Local Generalization. [PUB] [CODE]
• Problem-Parameter-Free Federated Learning. [PUB]
• Adaptive Gradient Clipping for Robust Federated Learning. [PUB]
• Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees. [PUB]
• LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression. [PUB]
• Group Distributionally Robust Dataset Distillation with Risk Minimization. [PUB]
• GRAIN: Exact Graph Reconstruction from Gradients. [PUB]
• Towards Faster Decentralized Stochastic Optimization with Communication Compression. [PUB]
• Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization. [PUB]
• Many-Objective Multi-Solution Transport. [PUB]
• Query-based Knowledge Transfer for Heterogeneous Learning Environments. [PUB]
• Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting. [PUB]
• Federated Granger Causality Learning For Interdependent Clients With State Space Representation. [PUB]
• Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization. [PUB]
• Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization. [PUB]
• On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning. [PUB]
• PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models. [PUB]
• Differentially Private Federated Learning with Time-Adaptive Privacy Spending. [PUB]
• Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies. [PUB]
• Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis. [PUB]
• On the Byzantine-Resilience of Distillation-Based Federated Learning. [PUB]
• Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [PUB]
• Event-Driven Online Vertical Federated Learning. [PUB]
• On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations. [PUB]
• Federated Domain Generalization with Data-free On-server Matching Gradient. [PUB]
• Unlocking the Potential of Model Calibration in Federated Learning. [PUB]
• FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking. [PUB]
• Understanding the Stability-based Generalization of Personalized Federated Learning. [PUB]
• Federated Residual Low-Rank Adaption of Large Language Models. [PUB]
• FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine. [PUB]
• Vertical Federated Learning with Missing Features During Training and Inference. [PUB] [CODE]
• Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost. [PUB]
• Selective Aggregation for Low-Rank Adaptation in Federated Learning. [PUB] [CODE]
• Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models. [PUB]
• Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection. [PUB]
• Debiasing Federated Learning with Correlated Client Participation. [PUB]
• Decoupled Subgraph Federated Learning. [PUB]
• Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning. [PUB]
• Towards Federated RLHF with Aggregated Client Preference for LLMs. [PUB]
• SparsyFed: Sparse Adaptive Federated Learning. [PUB]
• Can Textual Gradient Work in Federated Learning?. [PUB]
• Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models. [PUB] [CODE]
• Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning. [PUB]
• Connecting Federated ADMM to Bayes. [PUB]
• Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning. [PUB]
• Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond. [PUB]
• Federated Few-Shot Class-Incremental Learning. [PUB]
• Federated Residual Low-Rank Adaptation of Large Language Models. [PUB]
• Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models. [PUB]
• EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models. [PUB]
• LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging. [PUB] [CODE]
• MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation. [PUB]
• Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning. [PUB] [CODE]
• Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace. [PUB] [CODE]
• Model merging with SVD to tie the Knots. [PUB] [CODE]
• MrT5: Dynamic Token Merging for Efficient Byte-level Language Models. [PUB]
• Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine. [PUB]
• REMEDY: Recipe Merging Dynamics in Large Vision-Language Models. [PUB]
• Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models. [PUB]

TPAMI

• DFedADMM: Dual Constraint Controlled Model Inconsistency for Decentralize Federated Learning. [PUB]
• Federated Multi-View K-Means Clustering. [PUB]
• FedID: Enhancing Federated Learning Security Through Dynamic Identification. [PUB]
• Medical Federated Model With Mixture of Personalized and Shared Components. [PUB]
• Re-Fed+: A Better Replay Strategy for Federated Incremental Learning. [PUB]
• Robust Asymmetric Heterogeneous Federated Learning With Corrupted Clients. [PUB]
• Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation. [PUB]
• Toward the Flatter Landscape and Better Generalization in Federated Learning Under Client-Level Differential Privacy. [PUB]
• VQ-FedDiff: Federated Learning Algorithm of Diffusion Models With Client-Specific Vector-Quantized Conditioning. [PUB]
• NICE: Improving Panoptic Narrative Detection and Segmentation With Cascading Collaborative Learning. [PUB]

2024

colt

• The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication. [PUB]

machine learning

• Aligning model outputs for class imbalanced non-IID federated learning. [PUB]
• Communication-efficient clustered federated learning via model distance. [PUB]
• Federated learning with superquantile aggregation for heterogeneous data. [PUB]
• Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. [PUB]

UAI

• FedAST: Federated Asynchronous Simultaneous Training. [PUB]
• On Convergence of Federated Averaging Langevin Dynamics. [PUB]
• On the Convergence of Hierarchical Federated Learning with Partial Worker Participation. [PUB]
• Pure Exploration in Asynchronous Federated Bandits. [PUB]

NeurIPS

• One-shot Federated Learning via Synthetic Distiller-Distillate Communication. [PUB]
• Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data. [PUB]
• FedGMKD: An Efficient Prototype Federated Learning Framework through Knowledge Distillation and Discrepancy-Aware Aggregation. [PUB]
• Improving Generalization in Federated Learning with Model-Data Mutual Information Regularization: A Posterior Inference Approach. [PUB]
• Federated Model Heterogeneous Matryoshka Representation Learning. [PUB]
• Federated Graph Learning for Cross-Domain Recommendation. [PUB]
• FedGMark: Certifiably Robust Watermarking for Federated Graph Learning. [PUB]
• Dual-Personalizing Adapter for Federated Foundation Models. [PUB]
• Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning. [PUB]
• Taming the Long Tail in Human Mobility Prediction. [PUB]
• Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning. [PUB]
• Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution. [PUB]
• DoFIT: Domain-aware Federated Instruction Tuning with Alleviated Catastrophic Forgetting. [PUB]
• Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability. [PUB]
• Federated Transformer: Multi-Party Vertical Federated Learning on Practical Fuzzily Linked Data. [PUB]
• FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction. [PUB]
• Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. [PUB]
• FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations. [PUB] [CODE]
• Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains. [PUB]
• pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning. [PUB]
• Why Go Full? Elevating Federated Learning Through Partial Network Updates. [PUB]
• FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion. [PUB]
• FedSSP: Federated Graph Learning with Spectral Knowledge and Personalized Preference. [PUB]
• Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. [PUB]
• A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs. [PUB]
• Private and Personalized Frequency Estimation in a Federated Setting. [PUB]
• The Sample-Communication Complexity Trade-off in Federated Q-Learning. [PUB]
• Federated Ensemble-Directed Offline Reinforcement Learning. [PUB]
• Federated Black-Box Adaptation for Semantic Segmentation. [PUB]
• Thinking Forward: Memory-Efficient Federated Finetuning of Language Models. [PUB] [CODE]
• Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method. [PUB]
• Optimal Design for Human Preference Elicitation. [PUB]
• Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration. [PUB]
• Personalized Federated Learning via Feature Distribution Adaptation. [PUB]
• SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning. [PUB]
• A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings. [PUB]
• RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation. [PUB]
• FedGTST: Boosting Global Transferability of Federated Models via Statistics Tuning. [PUB]
• End-to-end Learnable Clustering for Intent Learning in Recommendation. [PUB]
• FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation. [PUB]
• Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting. [PUB]
• FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection. [PUB] [CODE]
• A Swiss Army Knife for Heterogeneous Federated Learning: Flexible Coupling via Trace Norm. [PUB]
• FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction. [PUB]
• Low Precision Local Training is Enough for Federated Learning. [PUB] [CODE]
• Resource-Aware Federated Self-Supervised Learning with Global Class Representations. [PUB]
• On the Necessity of Collaboration for Online Model Selection with Decentralized Data. [PUB]
• The Power of Extrapolation in Federated Learning. [PUB]
• (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning. [PUB]
• On Sampling Strategies for Spectral Model Sharding. [PUB]
• Customizing Language Models with Instance-wise LoRA for Sequential Recommendation. [PUB]
• SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead. [PUB]
• HYDRA-FL: Hybrid Knowledge Distillation for Robust and Accurate Federated Learning. [PUB]
• Stabilized Proximal-Point Methods for Federated Optimization. [PUB]
• DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices. [PUB]
• Parameter Disparities Dissection for Backdoor Defense in Heterogeneous Federated Learning. [PUB]
• Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGD. [PUB]
• FedAvP: Augment Local Data via Shared Policy in Federated Learning. [PUB]
• CoBo: Collaborative Learning via Bilevel Optimization. [PUB]
• Convergence Analysis of Split Federated Learning on Heterogeneous Data. [PUB]
• Communication-Efficient Federated Group Distributionally Robust Optimization. [PUB]
• Ferrari: Federated Feature Unlearning via Optimizing Feature Sensitivity. [PUB] [CODE]
• Federated Learning over Connected Modes. [PUB]
• Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning. [PUB]
• Does Egalitarian Fairness Lead to Instability? The Fairness Bounds in Stable Federated Learning Under Altruistic Behaviors. [PUB]
• Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups. [PUB]
• DataStealing: Steal Data from Diffusion Models in Federated Learning with Multiple Trojans. [PUB] [CODE]
• Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning. [PUB]
• Hierarchical Federated Learning with Multi-Timescale Gradient Correction. [PUB]
• HyperPrism: An Adaptive Non-linear Aggregation Framework for Distributed Machine Learning over Non-IID Data and Time-varying Communication Links. [PUB]
• SPEAR: Exact Gradient Inversion of Batches in Federated Learning. [PUB]
• Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis. [PUB]
• Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views. [PUB] [CODE]
• Confusion-Resistant Federated Learning via Diffusion-Based Data Harmonization on Non-IID Data. [PUB]
• Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning. [PUB]
• Free-Rider and Conflict Aware Collaboration Formation for Cross-Silo Federated Learning. [PUB]
• Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift. [PUB] [CODE]
• Heterogeneity-Guided Client Sampling: Towards Fast and Efficient Non-IID Federated Learning. [PUB]
• FACT or Fiction: Can Truthful Mechanisms Eliminate Federated Free Riding?. [PUB]
• Active preference learning for ordering items in- and out-of-sample. [PUB]
• Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources. [PUB]
• Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients. [PUB]
• Revisiting Ensembling in One-Shot Federated Learning. [PUB]
• FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models. [PUB]
• $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [PUB]
• FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection. [PUB]
• pfl-research: simulation framework for accelerating research in Private Federated Learning. [PUB]
• $C2M3$: Cycle-Consistent Multi-Model Merging. [PUB]
• A Kernel Perspective on Distillation-based Collaborative Learning. [PUB]
• Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling. [PUB] [CODE]
• Collaborative Refining for Learning from Inaccurate Labels. [PUB]
• Communication Efficient Distributed Training with Distributed Lion. [PUB]
• DAGER: Exact Gradient Inversion for Large Language Models. [PUB]
• EMR-Merging: Tuning-Free High-Performance Model Merging. [PUB]
• Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration. [PUB]
• Gradient-free Decoder Inversion in Latent Diffusion Models. [PUB]
• Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement Learning. [PUB]
• Parameter Competition Balancing for Model Merging. [PUB]
• SLowcalSGD : Slow Query Points Improve Local-SGD for Stochastic Convex Optimization. [PUB]
• Twin-Merging: Dynamic Integration of Modular Expertise in Model Merging. [PUB]
• Unravelling in Collaborative Learning. [PUB]

NeurIPS workshop

• Momentum Approximation in Asynchronous Private Federated Learning. [PUB]
• Cohort Squeeze: Beyond a Single Communication Round per Cohort in Cross-Device Federated Learning. [PUB]
• Federated Learning with Generative Content. [PUB]
• Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models. [PUB]
• Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models. [PUB]
• Defection-Free Collaboration between Competitors in a Learning System. [PUB]
• On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments. [PUB]
• EncCluster: Bringing Functional Encryption in Federated Foundational Models. [PUB]
• Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models. [PUB]
• Hot Pluggable Federated Learning. [PUB]
• Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees. [PUB]
• The Future of Large Language Model Pre-training is Federated. [PUB]
• Collaborative Learning with Shared Linear Representations: Statistical Rates and Optimal Algorithms. [PUB]
• The SynapticCity Phenomenon: When All Foundation Models Marry Federated Learning and Blockchain. [PUB]
• ZOOPFL: Exploring Black-box Foundation Models for Personalized Federated Learning. [PUB]
• DeComFL: Federated Learning with Dimension-Free Communication. [PUB]
• Improving Group Connectivity for Generalization of Federated Deep Learning. [PUB]
• MAP: Model Merging with Amortized Pareto Front Using Limited Computation. [PUB]
• OPA: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning. [PUB]
• Adaptive Hybrid Model Pruning in Federated Learning through Loss Exploration. [PUB]
• Worldwide Federated Training of Language Models. [PUB]
• FedStein: Enhancing Multi-Domain Federated Learning Through James-Stein Estimator. [PUB]
• Enhancing Causal Discovery in Federated Settings with Limited Local Samples. [PUB]
• $ exttt{pfl-research}$: simulation framework for accelerating research in Private Federated Learning. [PUB]
• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing. [PUB]

JMLR

• FedCBO: Reaching Group Consensus in Clustered Federated Learning through Consensus-based Optimization. [PUB]
• A Random Projection Approach to Personalized Federated Learning: Enhancing Communication Efficiency, Robustness, and Fairness. [PUB]
• Compressed and distributed least-squares regression: convergence rates with applications to federated learning. [PUB]
• Countering the Communication Bottleneck in Federated Learning: A Highly Efficient Zero-Order Optimization Technique. [PUB]
• Federated Automatic Differentiation. [PUB]
• Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning. [PUB]
• Distributed Gaussian Mean Estimation under Communication Constraints: Optimal Rates and Communication-Efficient Algorithms. [PUB]

ICML

• Effective Federated Graph Matching. [PUB]
• Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation. [PUB]
• Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients. [PUB]
• FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language Models. [PUB]
• Bridging Model Heterogeneity in Federated Learning via Uncertainty-based Asymmetrical Reciprocity Learning. [PUB]
• A New Theoretical Perspective on Data Heterogeneity in Federated Optimization. [PUB]
• Enhancing Storage and Computational Efficiency in Federated Multimodal Learning for Large-Scale Models. []
• Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments. [PUB]
• Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates. [PUB]
• Provable Benefits of Local Steps in Heterogeneous Federated Learning for Neural Networks: A Feature Learning Perspective. [PUB]
• Accelerating Federated Learning with Quick Distributed Mean Estimation. [PUB]
• Fair Federated Learning via the Proportional Veto Core. [PUB]
• AegisFL: Efficient and Flexible Privacy-Preserving Byzantine-Robust Cross-silo Federated Learning. [PUB] [CODE]
• Recovering Labels from Local Updates in Federated Learning. [PUB]
• FedMBridge: Bridgeable Multimodal Federated Learning. [PUB]
• Harmonizing Generalization and Personalization in Federated Prompt Learning. [PUB]
• Locally Estimated Global Perturbations are Better than Local Perturbations for Federated Sharpness-aware Minimization. [PUB]
• Accelerating Heterogeneous Federated Learning with Closed-form Classifiers. [PUB]
• Federated Combinatorial Multi-Agent Multi-Armed Bandits. [PUB]
• A Doubly Recursive Stochastic Compositional Gradient Descent Method for Federated Multi-Level Compositional Optimization. [PUB]
• Private Heterogeneous Federated Learning Without a Trusted Server Revisited: Error-Optimal and Communication-Efficient Algorithms for Convex Losses. [PUB]
• FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering. [PUB]
• Pursuing Overall Welfare in Federated Learning through Sequential Decision Making. [PUB] [CODE]
• PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs. [PUB] [CODE]
• Self-Driven Entropy Aggregation for Byzantine-Robust Heterogeneous Federated Learning. [PUB]
• Overcoming Data and Model heterogeneities in Decentralized Federated Learning via Synthetic Anchors. [PUB]
• Federated Optimization with Doubly Regularized Drift Correction. [PUB]
• FedSC: Provable Federated Self-supervised Learning with Spectral Contrastive Objective over Non-i.i.d. Data. [PUB]
• Certifiably Byzantine-Robust Federated Conformal Prediction. [PUB]
• Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning. [PUB]
• Clustered Federated Learning via Gradient-based Partitioning. [PUB]
• Recurrent Early Exits for Federated Learning with Heterogeneous Clients. [PUB]
• Rethinking the Flat Minima Searching in Federated Learning. [PUB]
• FedBAT: Communication-Efficient Federated Learning via Learnable Binarization. [PUB]
• Federated Representation Learning in the Under-Parameterized Regime. [PUB]
• FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees. [PUB]
• Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning. [PUB]
• SILVER: Single-loop variance reduction and application to federated learning. [PUB]
• SignSGD with Federated Defense: Harnessing Adversarial Attacks through Gradient Sign Decoding. [PUB]
• FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler. [PUB]
• Federated Continual Learning via Prompt-based Dual Knowledge Transfer. [PUB]
• Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes. [PUB]
• Decomposable Submodular Maximization in Federated Setting. [PUB]
• Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems. [PUB]
• Improved Modelling of Federated Datasets using Mixtures-of-Dirichlet-Multinomials. [PUB]
• Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!. [PUB]
• Byzantine Resilient and Fast Federated Few-Shot Learning. [PUB]
• Causally Motivated Personalized Federated Invariant Learning with Shortcut-Averse Information-Theoretic Regularization. [PUB]
• Ranking-based Client Imitation Selection for Efficient Federated Learning. [PUB]
• Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms. [PUB]
• FADAS: Towards Federated Adaptive Asynchronous Optimization. [PUB]
• Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices. [PUB]
• FedREDefense: Defending against Model Poisoning Attacks for Federated Learning using Model Update Reconstruction Error. [PUB]
• MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis. [PUB]
• Federated Neuro-Symbolic Learning. [PUB]
• Adaptive Group Personalization for Federated Mutual Transfer Learning. [PUB]
• Balancing Similarity and Complementarity for Federated Learning. [PUB]
• Federated Self-Explaining GNNs with Anti-shortcut Augmentations. [PUB]
• A Federated Stochastic Multi-level Compositional Minimax Algorithm for Deep AUC Maximization. [PUB]
• COALA: A Practical and Vision-Centric Federated Learning Platform. [PUB] [CODE]
• Collaborative Learning with Different Labeling Functions. [PUB]
• EvGGS: A Collaborative Learning Framework for Event-based Generalizable Gaussian Splatting. [PUB]
• Learning-Efficient Yet Generalizable Collaborative Filtering for Item Recommendation. [PUB]
• Localizing Task Information for Improved Model Merging and Compression. [PUB]
• Merging Multi-Task Models via Weight-Ensembling Mixture of Experts. [PUB]
• Relaxing the Accurate Imputation Assumption in Doubly Robust Learning for Debiased Collaborative Filtering. [PUB]
• Representation Surgery for Multi-Task Model Merging. [PUB]
• Socialized Learning: Making Each Other Better Through Multi-Agent Collaboration. [PUB]
• Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models. [PUB]

Mach Learn

• Secure and fast asynchronous Vertical Federated Learning via cascaded hybrid optimization. [PUB]
• Communication-efficient clustered federated learning via model distance. [PUB]
• Federated learning with superquantile aggregation for heterogeneous data. [PUB] [PDF] [CODE]
• Aligning model outputs for class imbalanced non-IID federated learning. [PUB]

TPAMI

• Federated Learning of Generalized Linear Causal Networks. [PUB]
• Cross-Modal Federated Human Activity Recognition. [PUB]
• Federated Gaussian Process: Convergence, Automatic Personalization and Multi-Fidelity Modeling. [PUB] [PDF] [CODE]
• The Impact of Adversarial Attacks on Federated Learning: A Survey. [PUB]
• Understanding and Mitigating Dimensional Collapse in Federated Learning. [PUB] [PDF] [CODE]
• No One Left Behind: Real-World Federated Class-Incremental Learning. [PUB] [PDF] [CODE]
• Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning. [PUB] [PDF] [CODE]
• Multi-Stage Asynchronous Federated Learning With Adaptive Differential Privacy. [PUB] [PDF] [CODE]
• A Bayesian Federated Learning Framework With Online Laplace Approximation. [PUB] [PDF] [CODE]
• Federated Feature Augmentation and Alignment. [PUB]
• Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark. [PUB]
• Gradient Inversion Attacks: Impact Factors Analyses and Privacy Enhancement. [PUB]
• Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification. [PUB]
• Improved Diversity-Promoting Collaborative Metric Learning for Recommendation. [PUB]

ICLR

• Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. [PUB]
• One-shot Empirical Privacy Estimation for Federated Learning. [PUB] [PDF]
• Stochastic Controlled Averaging for Federated Learning with Communication Compression. [PUB] [PDF]
• A Lightweight Method for Tackling Unknown Participation Statistics in Federated Averaging. [PUB] [PDF] [CODE]
• A Mutual Information Perspective on Federated Contrastive Learning. [PUB]
• Benchmarking Algorithms for Federated Domain Generalization. [PUB] [PDF] [CODE]
• Effective and Efficient Federated Tree Learning on Hybrid Data. [PUB] [PDF]
• Federated Recommendation with Additive Personalization. [PUB] [PDF] [CODE]
• Tackling the Data Heterogeneity in Asynchronous Federated Learning with Cached Update Calibration. [PUB] [SUPP]
• Federated Orthogonal Training: Mitigating Global Catastrophic Forgetting in Continual Federated Learning. [PUB] [SUPP] [PDF]
• Accurate Forgetting for Heterogeneous Federated Continual Learning. [PUB] [CODE]
• Federated Causal Discovery from Heterogeneous Data. [PUB] [PDF] [CODE]
• On Differentially Private Federated Linear Contextual Bandits. [PUB] [SUPP] [PDF]
• Incentivized Truthful Communication for Federated Bandits. [PUB] [PDF]
• Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting. [PUB]
• FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity. [PUB]
• Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [PUB] [PDF]
• Improving LoRA in Privacy-preserving Federated Learning. [PUB]
• FedWon: Triumphing Multi-domain Federated Learning Without Normalization. [PUB] [PDF]
• FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning. [PUB]
• FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler. [PUB] [PDF] [CODE] [PAGE]
• Bayesian Coreset Optimization for Personalized Federated Learning. [PUB]
• Layer-wise linear mode connectivity. [PUB] [PDF] [SUPP]
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation. [PUB] [PDF]
• Hiding in Plain Sight: Disguising Data Stealing Attacks in Federated Learning. [PUB] [SUPP] [PDF]
• Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning. [PUB] [PDF]
• Adaptive Federated Learning with Auto-Tuned Clients. [PUB] [SUPP] [PDF]
• Backdoor Federated Learning by Poisoning Backdoor-Critical Layers. [PUB] [SUPP] [PDF]
• Federated Q-Learning: Linear Regret Speedup with Low Communication Cost. [PUB] [SUPP] [PDF]
• FedImpro: Measuring and Improving Client Update in Federated Learning. [PUB] [PDF]
• Federated Wasserstein Distance. [PUB] [SUPP] [PDF]
• An improved analysis of per-sample and per-update clipping in federated learning. [PUB]
• FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation. [PUB] [SUPP]
• Internal Cross-layer Gradients for Extending Homogeneity to Heterogeneity in Federated Learning. [PUB] [PDF]
• Momentum Benefits Non-iid Federated Learning Simply and Provably. [PUB] [PDF]
• Communication-Efficient Federated Non-Linear Bandit Optimization. [PUB] [PDF]
• Fair and Efficient Contribution Valuation for Vertical Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. [PUB] [PDF]
• Learning Personalized Causally Invariant Representations for Heterogeneous Federated Clients. [PUB]
• PeFLL: Personalized Federated Learning by Learning to Learn. [PUB] [SUPP] [PDF]
• Communication-Efficient Gradient Descent-Accent Methods for Distributed Variational Inequalities: Unified Analysis and Local Updates. [PUB] [SUPP] [PDF]
• FedInverse: Evaluating Privacy Leakage in Federated Learning. [PUB] [SUPP]
• FedDA: Faster Adaptive Gradient Methods for Federated Constrained Optimization. [PUB] [SUPP] [PDF]
• Robust Training of Federated Models with Extremely Label Deficiency. [PUB] [PDF] [CODE]
• Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory. [PUB]
• Teach LLMs to Phish: Stealing Private Information from Language Models. [PUB]
• Like Oil and Water: Group Robustness Methods and Poisoning Defenses Don't Mix. [PUB]
• Accelerated Convergence of Stochastic Heavy Ball Method under Anisotropic Gradient Noise. [PUB] [PDF]
• Towards Eliminating Hard Label Constraints in Gradient Inversion Attacks. [PUB] [SUPP] [PDF] [CODE]
• Local Composite Saddle Point Optimization. [PUB] [PDF]
• Enhancing Neural Training via a Correlated Dynamics Model. [PUB] [PDF]
• EControl: Fast Distributed Optimization with Compression and Error Control. [PUB] [SUPP] [PDF]
• Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Bandit. [PUB]
• FedHyper: A Universal and Robust Learning Rate Scheduler for Federated Learning with Hypergradient Descent. [PUB] [SUPP] [PDF] [CODE]
• Heterogeneous Personalized Federated Learning by Local-Global Updates Mixing via Convergence Rate. [PUB]
• Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [PUB]
• Simple Minimax Optimal Byzantine Robust Algorithm for Nonconvex Objectives with Uniform Gradient Heterogeneity. [PUB]
• VFLAIR: A Research Library and Benchmark for Vertical Federated Learning. [PUB] [PDF] [CODE]
• Incentive-Aware Federated Learning with Training-Time Model Rewards. [PUB] [SUPP]
• VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarks. [PUB] [PDF] [CODE]
• FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data. [PUB] [SUPP] [PDF]
• Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition. [PUB]
• Federated Text-driven Prompt Generation for Vision-Language Models. [PUB]
• A Good Learner can Teach Better: Teacher-Student Collaborative Knowledge Distillation. [PUB]
• AdaMerging: Adaptive Model Merging for Multi-Task Learning. [PUB]
• CLAP: Collaborative Adaptation for Patchwork Learning. [PUB]
• CO2: Efficient Distributed Training with Full Communication-Computation Overlap. [PUB]
• Model Merging by Uncertainty-Based Gradient Matching. [PUB]
• ZipIt! Merging Models from Different Tasks without Training. [PUB]

2023

machine learning

• Ensemble and continual federated learning for classification tasks. [PUB]
• FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. [PUB]
• Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. [PUB]

NeurIPS

• SimFBO: Towards Simple, Flexible and Communication-efficient Federated Bilevel Learning. [PUB] [PDF] [SUPP]
• Mechanism Design for Collaborative Normal Mean Estimation. [PUB] [PDF]
• Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity. [PUB] [PDF] [CODE]
• Incentives in Federated Learning: Equilibria, Dynamics, and Mechanisms for Welfare Maximization. [PUB] [SUPP]
• Convergence Analysis of Sequential Federated Learning on Heterogeneous Data. [PUB] [PDF] [CODE]
• Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition. [PUB] [PDF] [CODE]
• Private Federated Frequency Estimation: Adapting to the Hardness of the Instance. [PUB] [SUPP] [PDF]
• Zeroth-Order Methods for Nondifferentiable, Nonconvex, and Hierarchical Federated Optimization. [PUB] [SUPP] [PDF]
• Incentivized Communication for Federated Bandits. [PUB] [PDF]
• Multiply Robust Federated Estimation of Targeted Average Treatment Effects. [PUB] [PDF]
• IBA: Towards Irreversible Backdoor Attacks in Federated Learning. [PUB] [SUPP] [CODE]
• EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning. [PUB] [SUPP] [PDF]
• Federated Linear Bandits with Finite Adversarial Actions. [PUB] [SUPP] [PDF]
• FedNAR: Federated Optimization with Normalized Annealing Regularization. [PUB] [SUPP] [PDF] [CODE]
• Guiding The Last Layer in Federated Learning with Pre-Trained Models. [PUB] [SUPP] [PDF] [CODE]
• Fine-Grained Theoretical Analysis of Federated Zeroth-Order Optimization. [PUB] [SUPP]
• Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection. [PUB] [SUPP] [PDF] [CODE]
• A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks. [PUB] [PDF] [CODE]
• Is Heterogeneity Notorious? Taming Heterogeneity to Handle Test-Time Shift in Federated Learning. [PUB] [SUPP]
• One-Pass Distribution Sketch for Measuring Data Heterogeneity in Federated Learning. [PUB] [SUPP] [CODE]
• Lockdown: Backdoor Defense for Federated Learning with Isolated Subspace Training. [PUB] [SUPP] [CODE]
• FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning. [PUB] [SUPP] [CODE]
• Towards Personalized Federated Learning via Heterogeneous Model Reassembly. [PUB] [SUPP] [PDF] [CODE]
• Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction. [PUB] [SUPP] [PDF]
• DFRD: Data-Free Robustness Distillation for Heterogeneous Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• A Unified Solution for Privacy and Communication Efficiency in Vertical Federated Learning. [PUB] [SUPP] [CODE]
• RECESS Vaccine for Federated Learning: Proactive Defense Against Model Poisoning Attacks. [PUB] [SUPP] [PDF]
• Federated Learning with Bilateral Curation for Partially Class-Disjoint Data. [PUB] [SUPP] [CODE]
• Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds. [PUB] [SUPP] [CODE]
• FedL2P: Federated Learning to Personalize. [PUB] [SUPP] [PDF] [CODE]
• Adaptive Test-Time Personalization for Federated Learning. [PUB] [PDF] [CODE]
• Federated Conditional Stochastic Optimization. [PUB] [SUPP] [PDF] [CODE]
• Federated Spectral Clustering via Secure Similarity Reconstruction. [PUB]
• Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM. [PUB] [SUPP] [PDF]
• FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks. [PUB] [SUPP] [PDF] [CODE]
• Federated Multi-Objective Learning. [PUB] [SUPP] [PDF]
• FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout. [PUB] [SUPP] [PDF] [CODE]
• Resolving the Tug-of-War: A Separation of Communication and Learning in Federated Learning. [PUB] [SUPP]
• Communication-Efficient Federated Bilevel Optimization with Global and Local Lower Level Problems. [PUB] [SUPP] [PDF]
• StableFDG: Style and Attention Based Learning for Federated Domain Generalization. [PUB] [PDF]
• Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization. [PUB] [SUPP] [PDF]
• DELTA: Diverse Client Sampling for Fasting Federated Learning. [PUB] [SUPP] [PDF]
• Federated Compositional Deep AUC Maximization. [PUB] [SUPP] [PDF]
• A3FL: Adversarially Adaptive Backdoor Attacks to Federated Learning. [PUB] [SUPP] [CODE]
• Flow: Per-instance Personalized Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Eliminating Domain Bias for Federated Learning in Representation Space. [PUB] [SUPP] [PDF] [CODE]
• Federated Learning with Manifold Regularization and Normalized Update Reaggregation. [PUB] [SUPP] [PDF]
• Structured Federated Learning through Clustered Additive Modeling. [PUB] [SUPP]
• Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer. [PUB] [SUPP] [PDF] [CODE]
• Dynamic Personalized Federated Learning with Adaptive Differential Privacy. [PUB] [SUPP] [CODE]
• Fed-CO$_{2}$ : Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Solving a Class of Non-Convex Minimax Optimization in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Federated Learning via Meta-Variational Dropout. [PUB] [CODE]
• Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates. [PUB] [SUPP] [PDF]
• SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning. [PUB] [CODE]
• Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense. [PUB] [SUPP]
• FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. [PUB] [PDF] [CODE]
• PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. [PUB] [SUPP] [PDF] [CODE] [解读]
• Spectral Co-Distillation for Personalized Federated Learning. [PUB]
• Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy. [PUB] [SUPP] [PDF]
• Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation. [PUB] [SUPP] [PDF] [CODE]
• (Amplified) Banded Matrix Factorization: A unified approach to private training. [PUB] [SUPP] [PDF]
• Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices. [PUB] [SUPP] [PDF] [CODE]
• Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation. [PUB] [SUPP] [PDF]
• Incentivizing Honesty among Competitors in Collaborative Learning and Optimization. [PUB] [SUPP] [PDF]
• Resilient Constrained Learning. [PUB] [SUPP] [PDF]
• A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting. [PUB] [SUPP] [PDF] [CODE]
• Collaboratively Learning Linear Models with Structured Missing Data. [PUB] [SUPP] [PDF] [CODE]
• Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy. [PUB] [SUPP] [PDF]
• Fast Optimal Locally Private Mean Estimation via Random Projections. [PUB] [SUPP] [PDF] [CODE]
• Contextual Stochastic Bilevel Optimization. [PUB] [SUPP] [PDF]
• Understanding Deep Gradient Leakage via Inversion Influence Functions. [PUB] [SUPP] [PDF] [CODE]
• Inner Product-based Neural Network Similarity. [PUB] [SUPP]
• Correlation Aware Sparsified Mean Estimation Using Random Projection. [PUB] [SUPP] [PDF] [CODE]
• TIES-Merging: Resolving Interference When Merging Models. [PUB] [SUPP] [PDF] [CODE]
• Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data. [PUB] [SUPP] [PDF] [CODE]
• Large-Scale Distributed Learning via Private On-Device LSH. [PUB] [SUPP] [PDF]
• Faster Relative Entropy Coding with Greedy Rejection Coding. [PUB] [SUPP] [PDF] [CODE]
• Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization. [PUB] [SUPP]
• Momentum Provably Improves Error Feedback!. [PUB] [SUPP] [PDF]
• Strategic Data Sharing between Competitors. [PUB] [SUPP] [PDF]
• H-nobs: Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets. [PUB]
• Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning. [PUB]
• Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. [PUB] [CODE]
• Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. [PUB]
• Birder: Communication-Efficient 1-bit Adaptive Optimizer for Practical Distributed DNN Training. [PUB]
• Collaborative Learning via Prediction Consensus. [PUB]
• Incentives in Private Collaborative Machine Learning. [PUB]
• Robust and Actively Secure Serverless Collaborative Learning. [PUB]
• Similarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities. [PUB]
• Swarm Reinforcement Learning for Adaptive Mesh Refinement. [PUB]

NeurIPS Datasets and Benchmarks

• Wyze Rule: Federated Rule Dataset for Rule Recommendation Benchmarking. [PUB] [SUPP] [DATASET]
• Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning. [PUB] [PDF] [DATASET] [CODE]

NeurIPS workshop

• Text-driven Prompt Generation for Vision-Language Models in Federated Learning. [PUB]
• HePCo: Data-Free Heterogeneous Prompt Consolidation for Continual Federated Learning. [PUB]
• Beyond Gradient and Priors in Privacy Attacks: Leveraging Pooler Layer Inputs of Language Models in Federated Learning. [PUB]
• FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data. [PUB]
• FedSoL: Bridging Global Alignment and Local Generality in Federated Learning. [PUB]
• One-shot Empirical Privacy Estimation for Federated Learning. [PUB]
• Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning. [PUB]
• SLoRA: Federated Parameter Efficient Fine-Tuning of Language Models. [PUB]
• The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. [PUB]
• Towards Building the FederatedGPT: Federated Instruction Tuning. [PUB]
• Federated Learning for Speech Recognition: Revisiting Current Trends Towards Large-Scale ASR. [PUB]
• LASER: Linear Compression in Wireless Distributed Optimization. [PUB]
• MARINA Meets Matrix Stepsizes: Variance Reduced Distributed Non-Convex Optimization. [PUB]
• TAMUNA: Doubly Accelerated Federated Learning with Local Training, Compression, and Partial Participation. [PUB]
• An Empirical Evaluation of Federated Contextual Bandit Algorithms. [PUB]
• RealFM: A Realistic Mechanism to Incentivize Data Contribution and Device Participation. [PUB]
• FDAPT: Federated Domain-adaptive Pre-training for Language Models. [PUB]
• Making Batch Normalization Great in Federated Deep Learning. [PUB]
• Correlated Noise Provably Beats Independent Noise for Differentially Private Learning. [PUB]
• Parameter Averaging Laws for Multitask Language Models. [PUB]
• Breaking Physical and Linguistic Borders: Multilingual Federated Prompt Tuning for Low-Resource Languages. [PUB]
• Beyond Parameter Averaging in Model Aggregation. [PUB]
• Augmenting Federated Learning with Pretrained Transformers. [PUB]
• Consensus Optimization at Representation: Improving Personalized Federated Learning via Data-Centric Regularization. [PUB]
• DPZero: Dimension-Independent and Differentially Private Zeroth-Order Optimization. [PUB]
• Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning. [PUB]
• FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System. [PUB]
• Learning Optimizers for Local SGD. [PUB]
• Exploring User-level Gradient Inversion with a Diffusion Prior. [PUB]
• User Inference Attacks on Large Language Models. [PUB]
• FedLDA: Personalized Federated Learning Through Collaborative Linear Discriminant Analysis. [PUB]
• Heterogeneous LoRA for Federated Fine-tuning of On-device Foundation Models. [PUB]
• Backdoor Threats from Compromised Foundation Models to Federated Learning. [PUB]
• MOFL/D: A Federated Multi-objective Learning Framework with Decomposition. [PUB]
• Absolute Variation Distance: an Inversion Attack Evaluation Metric for Federated Learning. [PUB]
• Fed3R: Recursive Ridge Regression for Federated Learning with strong pre-trained models. [PUB]
• FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning. [PUB]
• Private and Personalized Histogram Estimation in a Federated Setting. [PUB]

COLT

• The Aggregation–Heterogeneity Trade-off in Federated Learning. [PUB]

UAI

• FLASH: Automating federated learning using CASH. [PUB] [SUPP] [MATERIAL]
• Personalized federated domain adaptation for item-to-item recommendation. [PUB] [PDF] [SUPP] [MATERIAL] [CODE]
• Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning. [PUB] [PDF] [SUPP] [MATERIAL]
• Federated learning of models pre-trained on different features with consensus graphs. [PUB] [SUPP] [MATERIAL] [CODE]
• Fast Heterogeneous Federated Learning with Hybrid Client Selection. [PUB] [SUPP] [MATERIAL] [PDF]
• Learning To Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning. [PUB] [PDF] [SUPP] [MATERIAL] [CODE]

ICML

• Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape. [PUB] [PDF] [SLIDES]
• Analysis of Error Feedback in Federated Non-Convex Optimization with Biased Compression: Fast Convergence and Partial Participation. [PUB] [PDF]
• FedHPO-Bench: A Benchmark Suite for Federated Hyperparameter Optimization. [PUB] [PDF] [CODE]
• Federated Conformal Predictors for Distributed Uncertainty Quantification. [PUB] [PDF] [CODE]
• Federated Adversarial Learning: A Framework with Convergence Analysis. [PUB] [PDF]
• Federated Heavy Hitter Recovery under Linear Sketching. [PUB] [PDF] [CODE]
• Doubly Adversarial Federated Bandits. [PUB] [PDF] [CODE]
• Achieving Linear Speedup in Non-IID Federated Bilevel Learning. [PUB] [PDF]
• One-Shot Federated Conformal Prediction. [PUB] [PDF] [CODE]
• Federated Online and Bandit Convex Optimization. [PUB]
• Federated Linear Contextual Bandits with User-level Differential Privacy. [PUB] [PDF]
• Vertical Federated Graph Neural Network for Recommender System. [PUB] [PDF] [CODE]
• Communication-Efficient Federated Hypergradient Computation via Aggregated Iterative Differentiation. [PUB] [PDF]
• Towards Understanding Ensemble Distillation in Federated Learning. [PUB]
• Personalized Subgraph Federated Learning. [PUB] [PDF] [CODE]
• Conformal Prediction for Federated Uncertainty Quantification Under Label Shift. [PUB] [PDF]
• Secure Federated Correlation Test and Entropy Estimation. [PUB] [PDF]
• Out-of-Distribution Generalization of Federated Learning via Implicit Invariant Relationships. [PUB] [CODE]
• Personalized Federated Learning under Mixture of Distributions. [PUB] [PDF] [CODE]
• FedDisco: Federated Learning with Discrepancy-Aware Collaboration. [PUB] [PDF] [CODE]
• Anchor Sampling for Federated Learning with Partial Client Participation. [PUB] [PDF] [CODE]
• Private Federated Learning with Autotuned Compression. [PUB] [PDF]
• Fast Federated Machine Unlearning with Nonlinear Functional Theory. [PUB]
• On the Convergence of Federated Averaging with Cyclic Client Participation. [PUB] [PDF]
• Revisiting Weighted Aggregation in Federated Learning with Neural Networks. [PUB] [PDF] [CODE]
• The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond. [PUB] [PDF] [SLIDES]
• GuardHFL: Privacy Guardian for Heterogeneous Federated Learning. [PUB]
• Flash: Concept Drift Adaptation in Federated Learning. [PUB]
• DoCoFL: Downlink Compression for Cross-Device Federated Learning. [PUB] [PDF]
• FeDXL: Provable Federated Learning for Deep X-Risk Optimization. [PUB] [PDF] [CODE]
• No One Idles: Efficient Heterogeneous Federated Learning with Parallel Edge and Server Computation. [PUB] [CODE]
• Personalized Federated Learning with Inferred Collaboration Graphs. [PUB] [CODE]
• Optimizing the Collaboration Structure in Cross-Silo Federated Learning. [PUB] [PDF] [CODE] [SLIDES]
• TabLeak: Tabular Data Leakage in Federated Learning. [PUB] [PDF] [CODE]
• FedCR: Personalized Federated Learning Based on Across-Client Common Representation with Conditional Mutual Information Regularization. [PUB] [CODE]
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction. [PUB] [PDF]
• Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design. [PUB] [PDF] [CODE]
• SRATTA: Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning. [PUB] [PDF] [CODE]
• Improving the Model Consistency of Decentralized Federated Learning. [PUB] [PDF]
• Efficient Personalized Federated Learning via Sparse Model-Adaptation. [PUB] [PDF] [CODE]
• From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning. [PUB] [PDF] [CODE]
• LeadFL: Client Self-Defense against Model Poisoning in Federated Learning. [PUB] [CODE]
• Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning. [PUB] [PDF] [CODE]
• FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models. [PUB] [PDF]
• FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction. [PUB] [PDF] [CODE]
• Towards Unbiased Training in Federated Open-world Semi-supervised Learning. [PUB] [PDF] [SLIDES]
• Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. [PUB] [PDF]
• Surrogate Model Extension (SME): A Fast and Accurate Weight Update Attack on Federated Learning. [PUB] [PDF] [CODE]
• Fair yet Asymptotically Equal Collaborative Learning. [PUB] [PDF] [CODE]
• Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability. [PUB] [PDF]
• Adversarial Collaborative Learning on Non-IID Features. [PUB]
• XTab: Cross-table Pretraining for Tabular Transformers. [PUB] [PDF] [CODE]
• Momentum Ensures Convergence of SIGNSGD under Weaker Assumptions. [PUB]
• Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting. [PUB] [PDF] [CODE]
• LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning. [PUB] [PDF]
• FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks. [PUB]
• Addressing Budget Allocation and Revenue Allocation in Data Market Environments Using an Adaptive Sampling Algorithm. [PUB] [PDF] [CODE]
• Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models. [PUB]
• RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution. [PUB]
• Robust Collaborative Learning with Linear Gradient Overhead. [PUB] [CODE]

Mach Learn

• Ensemble and continual federated learning for classification tasks. [PUB] [PDF]
• FAC-fed: Federated adaptation for fairness and concept drift aware stream classification. [PUB]
• Robust federated learning under statistical heterogeneity via hessian-weighted aggregation. [PUB]

JMLR

• FedLab: A Flexible Federated Learning Framework :fire:. [PUB] [PDF] [CODE]
• Minimax Estimation for Personalized Federated Learning: An Alternative between FedAvg and Local Training?. [PUB]
• Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated Learning. [PUB] [PDF] [CODE]
• A First Look into the Carbon Footprint of Federated Learning. [PUB] [PDF]
• Attacks against Federated Learning Defense Systems and their Mitigation. [PUB] [CODE]
• A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates. [PUB] [PDF] [CODE]
• FedLab: A Flexible Federated Learning Framework. [PUB]
• A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points. [PUB]
• Multi-view Collaborative Gaussian Process Dynamical Systems. [PUB]
• Variational Inference for Deblending Crowded Starfields. [PUB]

TPAMI

• Tighter Regret Analysis and Optimization of Online Federated Learning. [PUB] [PDF]
• Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup. [PDF]
• Federated Learning Via Inexact ADMM. [PUB] [PDF] [CODE]
• FedIPR: Ownership Verification for Federated Deep Neural Network Models. [PUB] [PDF] [CODE] [解读]
• Decentralized Federated Averaging. [PUB] [PDF]
• Attribute-Guided Collaborative Learning for Partial Person Re-Identification. [PUB]
• Rethinking Collaborative Metric Learning: Toward an Efficient Alternative Without Negative Sampling. [PUB]

ICLR

• Personalized Federated Learning with Feature Alignment and Classifier Collaboration. [PUB] [CODE]
• MocoSFL: enabling cross-client collaborative self-supervised learning. [PUB] [CODE]
• Single-shot General Hyper-parameter Optimization for Federated Learning. [PUB] [PDF] [CODE]
• Where to Begin? Exploring the Impact of Pre-Training and Initialization in Federated. [PUB] [PDF] [CODE]
• FedExP: Speeding up Federated Averaging via Extrapolation. [PUB] [PDF] [CODE]
• Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection. [PUB] [CODE]
• DASHA: Distributed Nonconvex Optimization with Communication Compression and Optimal Oracle Complexity. [PUB] [PDF] [CODE]
• Machine Unlearning of Federated Clusters. [PUB] [PDF] [CODE]
• Federated Neural Bandits. [PUB] [PDF] [CODE]
• FedFA: Federated Feature Augmentation. [PUB] [PDF] [CODE]
• Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach. [PUB] [PDF] [CODE]
• Better Generative Replay for Continual Federated Learning. [PUB] [CODE]
• Federated Learning from Small Datasets. [PUB] [PDF]
• Federated Nearest Neighbor Machine Translation. [PUB] [PDF]
• Meta Knowledge Condensation for Federated Learning. [PUB] [PDF]
• Test-Time Robust Personalization for Federated Learning. [PUB] [PDF] [CODE]
• DepthFL : Depthwise Federated Learning for Heterogeneous Clients. [PUB]
• Towards Addressing Label Skews in One-Shot Federated Learning. [PUB] [CODE]
• Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning. [PUB] [PDF] [CODE]
• Panning for Gold in Federated Learning: Targeted Text Extraction under Arbitrarily Large-Scale Aggregation. [PUB] [CODE]
• SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication. [PUB] [PDF] [CODE]
• Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses. [PUB] [PDF] [CODE]
• Effective passive membership inference attacks in federated learning against overparameterized models. [PUB]
• FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification. [PUB] [PDF] [CODE]
• Multimodal Federated Learning via Contrastive Representation Ensemble. [PUB] [PDF] [CODE]
• Faster federated optimization under second-order similarity. [PUB] [PDF] [CODE]
• FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy. [PUB] [CODE]
• The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation. [PUB] [PDF] [CODE]
• PerFedMask: Personalized Federated Learning with Optimized Masking Vectors. [PUB] [CODE]
• EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data. [PUB] [CODE]
• FedDAR: Federated Domain-Aware Representation Learning. [PUB] [PDF] [CODE]
• Share Your Representation Only: Guaranteed Improvement of the Privacy-Utility Tradeoff in Federated Learning. [PUB] [CODE]
• FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning. [PUB] [PDF] [CODE]
• Generalization Bounds for Federated Learning: Fast Rates, Unparticipating Clients and Unbounded Losses. [PUB]
• Efficient Federated Domain Translation. [PUB] [CODE]
• On the Importance and Applicability of Pre-Training for Federated Learning. [PUB] [PDF] [CODE]
• Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models. [PUB] [PDF] [CODE]
• A Statistical Framework for Personalized Federated Learning and Estimation: Theory, Algorithms, and Privacy. [PUB] [PDF]
• Instance-wise Batch Label Restoration via Gradients in Federated Learning. [PUB] [CODE]
• Data-Free One-Shot Federated Learning Under Very High Statistical Heterogeneity. [PUB]
• CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning. [PUB] [PDF] [CODE]
• Sparse Random Networks for Communication-Efficient Federated Learning. [PUB] [PDF] [CODE]
• Combating Exacerbated Heterogeneity for Robust Decentralized Models. [PUB] [CODE]
• Hyperparameter Optimization through Neural Network Partitioning. [PUB] [PDF]
• Does Decentralized Learning with Non-IID Unlabeled Data Benefit from Self Supervision?. [PUB] [PDF] [CODE]
• Variance Reduction is an Antidote to Byzantines: Better Rates, Weaker Assumptions and Communication Compression as a Cherry on the Top. [PUB] [PDF] [CODE]
• Dual Diffusion Implicit Bridges for Image-to-Image Translation. [PUB] [PDF] [CODE]
• Bias Propagation in Federated Learning. [PUB]
• Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. [PUB]
• Where to Begin? On the Impact of Pre-Training and Initialization in Federated Learning. [PUB]
• Dataless Knowledge Fusion by Merging Weights of Language Models. [PUB]
• DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation. [PUB]
• Git Re-Basin: Merging Models modulo Permutation Symmetries. [PUB]
• TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations. [PUB]
• Why (and When) does Local SGD Generalize Better than SGD?. [PUB]

2022

colt

• Statistical Estimation and Online Inference via Local SGD. [PUB]

Mach Learn

• An accurate, scalable and verifiable protocol for federated differentially private averaging. [PUB] [PDF]

machine learning

• An accurate, scalable and verifiable protocol for federated differentially private averaging. [PUB]

UAI

• Federated online clustering of bandits. [PUB] [PDF] [CODE]
• Privacy-aware compression for federated data analysis. [PUB] [PDF] [CODE]
• Faster non-convex federated learning via global and local momentum. [PUB] [PDF]
• Fedvarp: Tackling the variance due to partial client participation in federated learning. [PUB] [PDF]
• SASH: Efficient secure aggregation based on SHPRG for federated learning. [PUB] [PDF]
• Bayesian federated estimation of causal effects from observational data. [PUB] [PDF]

TPAMI

• Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning. [PUB]
• Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning. [PUB] [CODE]
• Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification. [PUB]

NeurIPS

• Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox. [PUB] [PDF]
• LAMP: Extracting Text from Gradients with Language Model Priors. [PUB] [CODE]
• FedAvg with Fine Tuning: Local Updates Lead to Representation Learning. [PUB] [PDF]
• On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond. [PUB] [PDF]
• Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams. [PUB] [CODE]
• Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks. [PUB] [PDF]
• Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective. [PUB]
• Subspace Recovery from Heterogeneous Data with Non-isotropic Noise. [PUB] [PDF]
• EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization. [PUB] [PDF]
• On-Demand Sampling: Learning Optimally from Multiple Distributions. [PUB] [CODE]
• Improved Utility Analysis of Private CountSketch. [PUB] [PDF] [CODE]
• Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning. [PUB] [CODE]
• Decentralized Local Stochastic Extra-Gradient for Variational Inequalities. [PUB] [PDF]
• BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression. [PUB] [PDF] [CODE]
• Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning. [PUB] [PDF]
• Near-Optimal Collaborative Learning in Bandits. [PUB] [PDF] [CODE]
• Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees. [PUB] [PDF]
• Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. [PUB] [CODE]
• FedPop: A Bayesian Approach for Personalised Federated Learning. [PUB] [PDF]
• Fairness in Federated Learning via Core-Stability. [PUB] [CODE]
• SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning. [PUB] [PDF]
• FedRolex: Model-Heterogeneous Federated Learning with Rolling Submodel Extraction. [PUB] [CODE]
• On Sample Optimality in Personalized Collaborative and Federated Learning. [PUB]
• DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing. [PUB] [PDF]
• FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning. [PUB]
• Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning. [PUB] [PDF]
• VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely?. [PUB] [CODE]
• DENSE: Data-Free One-Shot Federated Learning. [PUB] [PDF]
• CalFAT: Calibrated Federated Adversarial Training with Label Skewness. [PUB] [PDF]
• SAGDA: Achieving O(ϵ−2) Communication Complexity in Federated Min-Max Learning. [PUB] [PDF]
• Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning. [PUB] [PDF]
• Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness. [PUB]
• Federated Submodel Optimization for Hot and Cold Data Features. [PUB]
• BooNTK: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. [PUB] [PDF]
• Byzantine-tolerant federated Gaussian process regression for streaming data. [PUB] [CODE]
• SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression. [PUB] [PDF]
• Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering. [PUB] [PDF] [CODE]
• Communication Efficient Federated Learning for Generalized Linear Bandits. [PUB] [CODE]
• Recovering Private Text in Federated Learning of Language Models. [PUB] [PDF] [CODE]
• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach. [PUB] [PDF]
• Global Convergence of Federated Learning for Mixed Regression. [PUB] [PDF]
• Resource-Adaptive Federated Learning with All-In-One Neural Composition. [PUB]
• Self-Aware Personalized Federated Learning. [PUB] [PDF]
• A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning. [PUB] [PDF]
• An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects. [PUB]
• Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning. [PUB] [PDF]
• Personalized Online Federated Multi-Kernel Learning. [PUB]
• SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training. [PUB] [PDF] [CODE]
• A Unified Analysis of Federated Learning with Arbitrary Client Participation. [PUB] [PDF]
• Preservation of the Global Knowledge by Not-True Distillation in Federated Learning. [PUB] [PDF] [CODE]
• FedSR: A Simple and Effective Domain Generalization Method for Federated Learning. [PUB] [CODE]
• Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching. [PUB] [PDF] [CODE]
• A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits. [PUB] [PDF]
• Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework. [PUB]
• On Privacy and Personalization in Cross-Silo Federated Learning. [PUB] [PDF]
• A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning. [PUB] [PDF] [CODE]
• Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching. [PUB] [CODE]
• FLAIR: Federated Learning Annotated Image Repository. [PUB]
• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [PUB]
• Personalized Online Federated Learning with Multiple Kernels. [PUB]
• pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning. [PUB]
• TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels. [PUB]
• A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks. [PUB]
• Collaborative Learning by Detecting Collaboration Partners. [PUB]
• Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints. [PUB]
• Communication Efficient Distributed Learning for Kernelized Contextual Bandits. [PUB]
• Communication-efficient distributed eigenspace estimation with arbitrary node failures. [PUB]
• GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations. [PUB]
• Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning. [PUB]
• Merging Models with Fisher-Weighted Averaging. [PUB]
• The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm. [PUB]
• Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning. [PUB]
• SAGDA: Achieving $\mathcal{O}(\epsilon{-2})$ Communication Complexity in Federated Min-Max Learning. [PUB]
• Taming Fat-Tailed ("Heavier-Tailed" with Potentially Infinite Variance) Noise in Federated Learning. [PUB]

NeurIPS Datasets and Benchmarks

• FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings. [PUB] [CODE]

ICML

• A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. [PUB] [PDF] [CODE]
• Fast Composite Optimization and Statistical Recovery in Federated Learning. [PUB] [PDF] [CODE]
• Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning. [PUB] [PDF] [CODE]
• The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning :fire:. [PUB] [PDF] [CODE] [SLIDE]
• The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation. [PUB] [PDF] [CODE]
• DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training. [PUB] [PDF] [CODE]
• FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning. [PUB] [PDF] [CODE]
• DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning. [PUB] [PDF] [SLIDE] [CODE]
• Accelerated Federated Learning with Decoupled Adaptive Optimization. [PUB] [PDF]
• Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling. [PUB] [PDF]
• Multi-Level Branched Regularization for Federated Learning. [PUB] [PDF] [CODE] [PAGE]
• FedScale: Benchmarking Model and System Performance of Federated Learning at Scale :fire:. [PUB] [PDF] [CODE]
• Federated Learning with Positive and Unlabeled Data. [PUB] [PDF] [CODE]
• Deep Neural Network Fusion via Graph Matching with Applications to Model Ensemble and Federated Learning. [PUB] [CODE]
• Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering. [PUB] [PDF] [CODE]
• Disentangled Federated Learning for Tackling Attributes Skew via Invariant Aggregation and Diversity Transferring. [PUB] [PDF] [CODE] [SLIDE] [解读]
• Architecture Agnostic Federated Learning for Neural Networks. [PUB] [PDF] [SLIDE]
• Personalized Federated Learning through Local Memorization. [PUB] [PDF] [CODE]
• Proximal and Federated Random Reshuffling. [PUB] [PDF] [CODE]
• Federated Learning with Partial Model Personalization. [PUB] [PDF] [CODE]
• Generalized Federated Learning via Sharpness Aware Minimization. [PUB] [PDF]
• FedNL: Making Newton-Type Methods Applicable to Federated Learning. [PUB] [PDF] [VIDEO] [SLIDE]
• Federated Minimax Optimization: Improved Convergence Analyses and Algorithms. [PUB] [PDF] [SLIDE]
• Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning. [PUB] [PDF] [CODE] [解读]
• FedNest: Federated Bilevel, Minimax, and Compositional Optimization. [PUB] [PDF] [CODE]
• EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning. [PUB] [PDF] [CODE]
• Communication-Efficient Adaptive Federated Learning. [PUB] [PDF]
• ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. [PUB] [PDF] [SLIDE] [CODE]
• Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification :fire:. [PUB] [PDF] [CODE]
• Anarchic Federated Learning. [PUB] [PDF]
• QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning. [PUB] [CODE]
• Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization. [PUB] [PDF]
• Neural Tangent Kernel Empowered Federated Learning. [PUB] [PDF] [CODE]
• Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy. [PUB] [PDF]
• Personalized Federated Learning via Variational Bayesian Inference. [PUB] [PDF] [SLIDE] [UC.]
• Federated Learning with Label Distribution Skew via Logits Calibration. [PUB]
• Neurotoxin: Durable Backdoors in Federated Learning. [PUB] [PDF] [CODE]
• Resilient and Communication Efficient Learning for Heterogeneous Federated Systems. [PUB]
• FedScale: Benchmarking Model and System Performance of Federated Learning at Scale. [PUB]
• Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification. [PUB]
• The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning. [PUB]
• 3PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregation. [PUB]
• Communication-efficient Distributed Learning for Large Batch Optimization. [PUB]
• PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration. [PUB] [CODE]

ICLR (oral)

• Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [PUB] [CODE]

ICLR

• Bayesian Framework for Gradient Leakage. [PUB] [PDF] [CODE]
• Federated Learning from only unlabeled data with class-conditional-sharing clients. [PUB] [CODE]
• FedChain: Chained Algorithms for Near-Optimal Communication Cost in Federated Learning. [PUB] [PDF]
• Acceleration of Federated Learning with Alleviated Forgetting in Local Training. [PUB] [PDF] [CODE]
• FedPara: Low-rank Hadamard Product for Communicatkion-Efficient Federated Learning. [PUB] [PDF] [CODE]
• An Agnostic Approach to Federated Learning with Class Imbalance. [PUB] [CODE]
• Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization. [PUB] [PDF] [CODE]
• Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models :fire:. [PUB] [PDF] [CODE]
• ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity. [PUB] [PDF]
• Diverse Client Selection for Federated Learning via Submodular Maximization. [PUB] [CODE]
• Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?. [PUB] [PDF] [CODE]
• Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions :fire:. [PUB] [CODE]
• Towards Model Agnostic Federated Learning Using Knowledge Distillation. [PUB] [PDF] [CODE]
• Divergence-aware Federated Self-Supervised Learning. [PUB] [PDF] [CODE]
• What Do We Mean by Generalization in Federated Learning? :fire:. [PUB] [PDF] [CODE]
• FedBABU: Toward Enhanced Representation for Federated Image Classification. [PUB] [PDF] [CODE]
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing. [PUB] [PDF] [CODE]
• Hybrid Local SGD for Federated Learning with Heterogeneous Communications. [PUB]
• On Bridging Generic and Personalized Federated Learning for Image Classification. [PUB] [PDF] [CODE]
• Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond. [PUB] [PDF]
• Diurnal or Nocturnal? Federated Learning of Multi-branch Networks from Periodically Shifting Distributions. [PUB]
• FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning. [PUB]
• Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters. [PUB]
• Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models. [PUB]
• What Do We Mean by Generalization in Federated Learning?. [PUB]
• SGD Can Converge to Local Maxima. [PUB]

ICLR Spotlight

• Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters. [PUB] [PDF] [PAGE] [解读]

2021

JMLR

• One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Them. [PUB] [CODE]
• Communication-Efficient Distributed Covariance Sketch, with Application to Distributed PCA. [PUB]
• Cooperative SGD: A Unified Framework for the Design and Analysis of Local-Update SGD Algorithms. [PUB]
• Estimating Uncertainty Intervals from Collaborating Networks. [PUB]
• FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection. [PUB]
• Hybrid Predictive Models: When an Interpretable Model Collaborates with a Black-box Model. [PUB]

tpami

• Task-Feature Collaborative Learning with Application to Personalized Attribute Prediction. [PUB]

UAI

• Constrained differentially private federated learning for low-bandwidth devices. [PUB] [PDF]
• Federated stochastic gradient Langevin dynamics. [PUB] [PDF]

ICLR

• Federated Learning Based on Dynamic Regularization. [PUB] [PDF] [CODE]
• Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning. [PUB] [PDF]
• HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients. [PUB] [PDF] [CODE]
• FedMix: Approximation of Mixup under Mean Augmented Federated Learning. [PUB] [PDF]
• Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms :fire:. [PUB] [PDF] [CODE]
• Adaptive Federated Optimization :fire:. [PUB] [PDF] [CODE]
• Personalized Federated Learning with First Order Model Optimization. [PUB] [PDF] [CODE] [UC.]
• FedBN: Federated Learning on Non-IID Features via Local Batch Normalization :fire:. [PUB] [PDF] [CODE]
• FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning. [PUB] [PDF] [CODE]
• Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. [PUB] [PDF] [CODE]
• Adaptive Federated Optimization. [PUB]
• FedBN: Federated Learning on Non-IID Features via Local Batch Normalization. [PUB] [CODE]
• Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning. [PUB] [CODE]
• A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning. [PUB]
• CaPC Learning: Confidential and Private Collaborative Learning. [PUB]
• Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks. [PUB]
• Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms. [PUB]

ICML

• KD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation. [PUB] [PDF] [CODE] [解读]
• Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. [PUB] [PDF] [VIDEO] [CODE]
• FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Analysis. [PUB] [PDF] [VIDEO]
• Personalized Federated Learning using Hypernetworks :fire:. [PUB] [PDF] [CODE] [PAGE] [VIDEO] [解读]
• Federated Composite Optimization. [PUB] [PDF] [CODE] [VIDEO] [SLIDE]
• Exploiting Shared Representations for Personalized Federated Learning. [PUB] [PDF] [CODE] [VIDEO]
• Data-Free Knowledge Distillation for Heterogeneous Federated Learning :fire:. [PUB] [PDF] [CODE] [VIDEO]
• Federated Continual Learning with Weighted Inter-client Transfer. [PUB] [PDF] [CODE] [VIDEO]
• Federated Deep AUC Maximization for Hetergeneous Data with a Constant Communication Complexity. [PUB] [PDF] [CODE] [VIDEO]
• Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning. [PUB] [PDF] [VIDEO]
• Federated Learning of User Verification Models Without Sharing Embeddings. [PUB] [PDF] [VIDEO]
• Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning. [PUB] [PDF] [CODE] [VIDEO]
• Ditto: Fair and Robust Federated Learning Through Personalization. [PUB] [PDF] [CODE] [VIDEO]
• Heterogeneity for the Win: One-Shot Federated Clustering. [PUB] [PDF] [VIDEO]
• The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation :fire:. [PUB] [PDF] [CODE] [VIDEO]
• Debiasing Model Updates for Improving Personalized Federated Training. [PUB] [CODE] [VIDEO]
• One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning. [PUB] [PDF] [CODE] [VIDEO]
• CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. [PUB] [PDF] [CODE] [VIDEO]
• Federated Learning under Arbitrary Communication Patterns. [PUB] [VIDEO]
• Data-Free Knowledge Distillation for Heterogeneous Federated Learning. [PUB]
• Personalized Federated Learning using Hypernetworks. [PUB]
• The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation. [PUB]
• Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data. [PUB]
• Communication-Efficient Distributed Optimization with Quantized Preconditioners. [PUB]
• Communication-Efficient Distributed SVD via Local Power Iterations. [PUB]
• Matrix Sketching for Secure Collaborative Machine Learning. [PUB]

NeurIPS

• CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression. [PUB] [PDF]
• Boosting with Multiple Sources. [PUB]
• DRIVE: One-bit Distributed Mean Estimation. [PUB] [CODE]
• Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. [PUB] [CODE]
• Gradient Inversion with Generative Image Prior. [PUB] [PDF] [CODE]
• Distributed Machine Learning with Sparse Heterogeneous Data. [PUB] [PDF]
• Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning. [PUB] [PDF]
• Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. [PUB]
• CAFE: Catastrophic Data Leakage in Vertical Federated Learning. [PUB] [CODE]
• Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee. [PUB] [PDF] [CODE]
• Optimality and Stability in Federated Learning: A Game-theoretic Approach. [PUB] [PDF] [CODE]
• QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning. [PUB] [PDF] [CODE] [解读]
• The Skellam Mechanism for Differentially Private Federated Learning :fire:. [PUB] [PDF] [CODE]
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data. [PUB] [PDF]
• STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning. [PUB] [PDF]
• Subgraph Federated Learning with Missing Neighbor Generation. [PUB] [PDF] [CODE] [解读]
• Evaluating Gradient Inversion Attacks and Defenses in Federated Learning :fire:. [PUB] [PDF] [CODE]
• Personalized Federated Learning With Gaussian Processes. [PUB] [PDF] [CODE]
• Differentially Private Federated Bayesian Optimization with Distributed Exploration. [PUB] [PDF] [CODE]
• Parameterized Knowledge Transfer for Personalized Federated Learning. [PUB] [PDF] [CODE]
• Federated Reconstruction: Partially Local Federated Learning :fire:. [PUB] [PDF] [CODE] [UC.]
• Fast Federated Learning in the Presence of Arbitrary Device Unavailability. [PUB] [PDF] [CODE]
• FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective. [PUB] [PDF] [CODE]
• FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout. [PUB] [PDF]
• Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients. [PUB] [PDF] [VIDEO]
• Federated Multi-Task Learning under a Mixture of Distributions. [PUB] [PDF] [CODE]
• Federated Graph Classification over Non-IID Graphs. [PUB] [PDF] [CODE] [解读]
• Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing. [PUB] [PDF] [CODE]
• On Large-Cohort Training for Federated Learning :fire:. [PUB] [PDF] [CODE]
• DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning. [PUB] [PDF] [CODE]
• PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization. [PUB] [VIDEO]
• Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis. [PUB] [PDF]
• Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning. [PUB] [PDF] [CODE]
• Federated Linear Contextual Bandits. [PUB] [PDF] [CODE]
• Few-Round Learning for Federated Learning. [PUB]
• Breaking the centralized barrier for cross-device federated learning. [PUB] [CODE] [VIDEO]
• Federated-EM with heterogeneity mitigation and variance reduction. [PUB] [PDF]
• Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning. [PUB] [PAGE] [SLIDE]
• FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization. [PUB] [PDF] [CODE]
• Catastrophic Data Leakage in Vertical Federated Learning. [PUB]
• Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. [PUB]
• Federated Reconstruction: Partially Local Federated Learning. [PUB]
• On Large-Cohort Training for Federated Learning. [PUB]
• The Skellam Mechanism for Differentially Private Federated Learning. [PUB]
• Asynchronous Decentralized SGD with Quantized and Local Updates. [PUB]
• CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum. [PUB]
• Communication-efficient SGD: From Local SGD to One-Shot Averaging. [PUB]
• Distributed Deep Learning In Open Collaborations. [PUB]
• Learning Collaborative Policies to Solve NP-hard Routing Problems. [PUB]
• Learning Distilled Collaboration Graph for Multi-Agent Perception. [PUB]
• Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer. [PUB]
• Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning). [PUB]

2020

ICLR

• Federated Adversarial Domain Adaptation. [PUB] [PDF] [CODE]
• DBA: Distributed Backdoor Attacks against Federated Learning. [PUB] [CODE]
• Fair Resource Allocation in Federated Learning :fire:. [PUB] [PDF] [CODE]
• Federated Learning with Matched Averaging :fire:. [PUB] [PDF] [CODE]
• Differentially Private Meta-Learning. [PUB] [PDF]
• Generative Models for Effective ML on Private, Decentralized Datasets :fire:. [PUB] [PDF] [CODE]
• On the Convergence of FedAvg on Non-IID Data :fire:. [PUB] [PDF] [CODE] [解读]
• Fair Resource Allocation in Federated Learning. [PUB]
• Federated Learning with Matched Averaging. [PUB]
• Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication. [PUB]
• Don't Use Large Mini-batches, Use Local SGD. [PUB]
• On the Convergence of FedAvg on Non-IID Data. [PUB]
• SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum. [PUB]

ICML

• FedBoost: A Communication-Efficient Algorithm for Federated Learning. [PUB] [VIDEO]
• FetchSGD: Communication-Efficient Federated Learning with Sketching. [PUB] [PDF] [VIDEO] [CODE]
• SCAFFOLD: Stochastic Controlled Averaging for Federated Learning. [PUB] [PDF] [VIDEO] [UC.] [解读]
• Federated Learning with Only Positive Labels. [PUB] [PDF] [VIDEO]
• From Local SGD to Local Fixed-Point Methods for Federated Learning. [PUB] [PDF] [SLIDE] [VIDEO]
• Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization. [PUB] [PDF] [SLIDE] [VIDEO]
• A Unified Theory of Decentralized SGD with Changing Topology and Local Updates. [PUB]
• Collaborative Machine Learning with Incentive-Aware Model Rewards. [PUB]
• Communication-Efficient Distributed PCA by Riemannian Optimization. [PUB]
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks. [PUB]
• Is Local SGD Better than Minibatch SGD?. [PUB]
• Manifold Identification for Ultimately Communication-Efficient Distributed Optimization. [PUB]

jmlr

• GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning. [PUB]

machine learning

• Communication-efficient distributed multi-task learning with matrix sparsity regularization. [PUB]

NeurIPS

• Differentially-Private Federated Linear Bandits. [PUB] [PDF] [CODE]
• Federated Principal Component Analysis. [PUB] [PDF] [CODE]
• FedSplit: an algorithmic framework for fast federated optimization. [PUB] [PDF]
• Federated Bayesian Optimization via Thompson Sampling. [PUB] [PDF] [CODE]
• Lower Bounds and Optimal Algorithms for Personalized Federated Learning. [PUB] [PDF]
• Robust Federated Learning: The Case of Affine Distribution Shifts. [PUB] [PDF] [CODE]
• An Efficient Framework for Clustered Federated Learning. [PUB] [PDF] [CODE]
• Distributionally Robust Federated Averaging :fire:. [PUB] [PDF] [CODE]
• Personalized Federated Learning with Moreau Envelopes :fire:. [PUB] [PDF] [CODE]
• Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach. [PUB] [PDF] [UC.]
• Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. [PUB] [PDF] [CODE] [解读]
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization :fire:. [PUB] [PDF] [CODE] [UC.]
• Attack of the Tails: Yes, You Really Can Backdoor Federated Learning. [PUB] [PDF]
• Federated Accelerated Stochastic Gradient Descent. [PUB] [PDF] [CODE] [VIDEO]
• Inverting Gradients - How easy is it to break privacy in federated learning? :fire:. [PUB] [PDF] [CODE]
• Ensemble Distillation for Robust Model Fusion in Federated Learning. [PUB] [PDF] [CODE]
• Throughput-Optimal Topology Design for Cross-Silo Federated Learning. [PUB] [PDF] [CODE]
• Distributionally Robust Federated Averaging. [PUB]
• Inverting Gradients - How easy is it to break privacy in federated learning?. [PUB]
• Personalized Federated Learning with Moreau Envelopes. [PUB]
• Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization. [PUB]
• A Scalable Approach for Privacy-Preserving Collaborative Machine Learning. [PUB]
• Minibatch vs Local SGD for Heterogeneous Distributed Learning. [PUB]
• ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. [PUB]

2019

colt

• Communication and Memory Efficient Testing of Discrete Distributions. [PUB]

iclr

• Local SGD Converges Fast and Communicates Little. [PUB]

ICML

• Bayesian Nonparametric Federated Learning of Neural Networks :fire:. [PUB] [PDF] [CODE]
• Analyzing Federated Learning through an Adversarial Lens :fire:. [PUB] [PDF] [CODE]
• Agnostic Federated Learning. [PUB] [PDF]
• Analyzing Federated Learning through an Adversarial Lens. [PUB]
• Bayesian Nonparametric Federated Learning of Neural Networks. [PUB]
• Collaborative Evolutionary Reinforcement Learning. [PUB]
• Learning to Collaborate in Markov Decision Processes. [PUB]
• On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization. [PUB]

jmlr

• Deep Reinforcement Learning for Swarm Systems. [PUB]

machine learning

• Collaborative topic regression for predicting topic-based social influence. [PUB]

neurips

• Communication trade-offs for Local-SGD with large step size. [PUB]
• Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. [PUB]
• Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients. [PUB]
• Communication-efficient Distributed SGD with Sketching. [PUB]
• Double Quantization for Communication-Efficient Distributed Optimization. [PUB]
• Learning to Optimize in Swarms. [PUB]
• Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization. [PUB]
• Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations. [PUB]
• Robust and Communication-Efficient Collaborative Learning. [PUB]

2018

icml

• An Alternative View: When Does SGD Escape Local Minima?. [PUB]

NeurIPS

• cpSGD: Communication-efficient and differentially-private distributed SGD. [PUB] [PDF]
• Collaborative Learning for Deep Neural Networks. [PUB]
• Gradient Sparsification for Communication-Efficient Distributed Optimization. [PUB]
• Improved Algorithms for Collaborative PAC Learning. [PUB]
• LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning. [PUB]
• Tight Bounds for Collaborative PAC Learning via Multiplicative Weights. [PUB]

uai

• Probabilistic Collaborative Representation Learning for Personalized Item Recommendation. [PUB]

2017

colt

• Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox. [PUB]

icml

• Communication-efficient Algorithms for Distributed Stochastic Principal Component Analysis. [PUB]

jmlr

• CoCoA: A General Framework for Communication-Efficient Distributed Optimization. [PUB]
• Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models. [PUB]

machine learning

• Collaborative topic regression for online recommender systems: an online and Bayesian approach. [PUB]

NeurIPS

• Federated Multi-Task Learning :fire:. [PUB] [PDF] [CODE]

uai

• Communication-Efficient Distributed Primal-Dual Algorithm for Saddle Point Problem. [PUB]

2016

jmlr

• The Statistical Performance of Collaborative Inference. [PUB]

2015

machine learning

• Random drift particle swarm optimization algorithm: convergence analysis and parameter selection. [PUB]

2014

icml

• Communication-Efficient Distributed Optimization using an Approximate Newton-type Method. [PUB]

machine learning

• Collaborative filtering with information-rich and information-sparse entities. [PUB]
• Collaborative information acquisition for data-driven decisions. [PUB]
• Detecting inappropriate access to electronic health records using collaborative filtering. [PUB]

2012

jmlr

• SVDFeature: a toolkit for feature-based collaborative filtering. [PUB]

2011

colt

• Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing. [PUB]

machine learning

• Editorial survey: swarm intelligence for data mining. [PUB]
• Particle swarm optimizer for variable weighting in clustering high-dimensional data. [PUB]

2009

icml

• Transfer learning for collaborative filtering via a rating-matrix generative model. [PUB]

jmlr

• A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization. [PUB]
• Particle Swarm Model Selection. [PUB]
• Scalable Collaborative Filtering Approaches for Large Recommender Systems. [PUB]

2008

machine learning

• A collaborative filtering framework based on both local user similarity and global user similarity. [PUB]

2006

jmlr

• Collaborative Multiagent Reinforcement Learning by Payoff Propagation. [PUB]

2005

colt

• Competitive Collaborative Learning. [PUB]

2004

machine learning

• Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. [PUB]

uai

• A Bayesian Approach toward Active Learning for Collaborative Filtering. [PUB]

2003

machine learning

• A Theoretical Analysis of Query Selection for Collaborative Filtering. [PUB]

uai

• Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes. [PUB]

2000

jmlr

• Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. [PUB]

tpami

• Merging and Splitting Eigenspace Models. [PUB]

2026

KDD

• Caesar: Optimizing Federated Learning via Low-deviation Compression. [PUB]
• Communication-efficient Federated Graph Classification via Generative Diffusion Modeling. [PUB]
• FedKDMR: Robust Federated Learning via Joint Knowledge Distillation & Model Recombination. [PUB]
• FedPRE: Robust Federated Graph Learning against Topological Corruption. [PUB]
• HAL: Accurate, Private, and Efficient Sample Alignment for Multimodal Federated Learning. [PUB]
• MFC: Mixed Federated Clustering based on Cross-modal Feature Decoupling. [PUB]
• Towards Privacy-Preserving and Heterogeneity-aware Split Federated Learning via Probabilistic Masking. [PUB]
• Two Heads Are Better Than One: Generalized Cross-Domain Federated Learning via Dual-Prototype. [PUB]
• Vertical Federated K-Means for Multi-View Data Guided by a K-Means Cost Bound after Projection. [PUB]
• MergeRec: Model Merging for Data-Isolated Cross-Domain Sequential Recommendation. [PUB]

WSDM

• Federated Watermarking of Deep Neural Networks with Distributed Verification. [PUB]
• Sharpness-aware Federated Graph Learning. [PUB]

2025

KDD

• A Unified Solution to Diverse Heterogeneities in One-Shot Federated Learning. [PUB] [CODE]
• Asymmetrical Reciprocity-based Federated Learning for Resolving Disparities in Medical Diagnosis. [PUB]
• Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training. [PUB]
• BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning. [PUB] [CODE]
• DarkDistill: Difficulty-Aligned Federated Early-Exit Network Training on Heterogeneous Devices. [PUB]
• FedAPM: Federated Learning via ADMM with Partial Model Personalization. [PUB]
• FedDiAL: Adaptive Federated Learning with Hierarchical Discriminative Network for Large Pre-trained Models. [PUB]
• Federated Continual Graph Learning. [PUB]
• FedGuCci: Making Local Models More Connected in Landscape for Federated Learning. [PUB] [CODE]
• FedKDD 2025: The 2025 International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. [PUB]
• FedMetro: Efficient Metro Passenger Flow Prediction via Federated Graph Learning. [PUB] [CODE]
• FedSC: Federated Learning with Semantic-Aware Collaboration. [PUB]
• FedVS: Towards Federated Vector Similarity Search with Filters. [PUB]
• FEZE: Alignment-Flexible Zero-Shot Vertical Federated Learning. [PUB]
• FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning. [PUB]
• Generalizing Personalized Federated Graph Augmentation via Min-max Adversarial Learning. [PUB]
• Gradients as An Action: Towards Communication-Efficient Federated Recommender Systems via Adaptive Action Sharing. [PUB] [CODE]
• GuardFGL: Similarity-driven Federated Graph Learning with Adversarial Robustness and Membership Privacy. [PUB]
• HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark. [PUB] [CODE]
• PARSIFAL: Private and Robust Sign Federated Learning. [PUB]
• PraFFL: A Preference-Aware Scheme in Fair Federated Learning. [PUB] [CODE]
• Proxy-Validated Importance-Aware Federated Sample Selection with Meta Learning. [PUB] [CODE]
• Runtime-Aware Pipeline for Vertical Federated Learning with Bounded Model Staleness. [PUB]
• Tackling Federated Long-Tailed Learning via Synthetic Feature-Based Decoupled Training. [PUB]
• Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression. [PUB] [CODE]
• Towards Collaborative Fairness in Federated Learning Under Imbalanced Covariate Shift. [PUB]
• Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning. [PUB]
• Multi-Branch Collaborative Learning Network for Video Quality Assessment in Industrial Video Search. [PUB]

WSDM

• Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation. [PUB]
• FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning. [PUB]
• Towards Personalized Federated Multi-Scenario Multi-Task Recommendation. [PUB]
• Density-aware and Cluster-based Federated Anomaly Detection on Data Streams. [PUB]
• Integrating Knowledge Graphs and Neuro-Symbolic AI: LDM Enables FAIR and Federated Research Data Management. [PUB]

2024

KDD

• Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination. [PUB]
• BadSampler: Harnessing the Power of Catastrophic Forgetting to Poison Byzantine-robust Federated Learning. [PUB]
• Federated Graph Learning with Structure Proxy Alignment. [PUB] [CODE]
• HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning. [PUB]
• FedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs. [PUB]
• Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization. [PUB] [CODE]
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning. [PUB] [CODE]
• On the Convergence of Zeroth-Order Federated Tuning for Large Language Models. [PUB]
• CASA: Clustered Federated Learning with Asynchronous Clients. [PUB]
• FLAIM: AIM-based Synthetic Data Generation in the Federated Setting. [PUB]
• Privacy-Preserving Federated Learning using Flower Framework. [PUB]
• FedSAC: Dynamic Submodel Allocation for Collaborative Fairness in Federated Learning. [PUB] [CODE]
• FedNLR: Federated Learning with Neuron-wise Learning Rates. [PUB]
• FedBiOT: LLM Local Fine-tuning in Federated Learning without Full Model. [PUB]
• FLea: Addressing Data Scarcity and Label Skew in Federated Learning via Privacy-preserving Feature Augmentation. [PUB] [CODE]
• Preventing Strategic Behaviors in Collaborative Inference for Vertical Federated Learning. [PUB]
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection. [PUB]
• FedRoLA: Robust Federated Learning Against Model Poisoning via Layer-based Aggregation. [PUB]
• FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction. [PUB] [CODE]
• OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning. [PUB] [CODE]
• Personalized Federated Continual Learning via Multi-Granularity Prompt. [PUB]
• Enabling Collaborative Test-Time Adaptation in Dynamic Environment via Federated Learning. [PUB] [CODE]
• GPFedRec: Graph-Guided Personalization for Federated Recommendation. [PUB] [CODE]
• Asynchronous Vertical Federated Learning for Kernelized AUC Maximization. [PUB]
• VertiMRF: Differentially Private Vertical Federated Data Synthesis. [PUB]
• FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics. [PUB]
• Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI Recommendations. [PUB]
• High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates. [PUB] [CODE]
• Unifying Graph Convolution and Contrastive Learning in Collaborative Filtering. [PUB] [CODE]

WSDM

• User Consented Federated Recommender System Against Personalized Attribute Inference Attack. [PUB] [PDF] [CODE]
• Guardian: Guarding against Gradient Leakage with Provable Defense for Federated Learning. [PUB]

2023

KDD

• Privacy Matters: Vertical Federated Linear Contextual Bandits for Privacy Protected Recommendation. [PUB] [PDF]

• FedDefender: Client-Side Attack-Tolerant Federated Learning. [PUB] [PDF] [CODE]

• FedAPEN: Personalized Cross-silo Federated Learning with Adaptability to Statistical Heterogeneity. [PUB] [CODE]

• FedPseudo: Privacy-Preserving Pseudo Value-Based Deep Learning Models for Federated Survival Analysis. [PUB] [PDF]

• ShapleyFL: Robust Federated Learning Based on Shapley Value. [PUB] [CODE]

• Federated Few-shot Learning. [PUB] [PDF] [CODE]

• Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity. [PUB]

• Personalized Federated Learning with Parameter Propagation. [PUB]

• Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining. [PUB] [PDF] [CODE]

• CriticalFL: A Critical Learning Periods Augmented Client Selection Framework for Efficient Federated Learning. [PUB] [PDF]

• FLAMES2Graph: An Interpretable Federated Multivariate Time Series Classification Framework. [PUB] [PDF]

• FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy. [PUB] [PDF] [CODE]

• Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Framework. [PUB] [PDF] [CODE]

• DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization. [PUB] [CODE]

• FS-REAL: Towards Real-World Cross-Device Federated Learning. [PUB] [PDF]

• FedMultimodal: A Benchmark for Multimodal Federated Learning. [PUB] [PDF] [CODE]

• PrivateRec: Differentially Private Model Training and Online Serving for Federated News Recommendation. [PUB] [PDF] [NEWS]

• Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks. [PUB] [PDF] [CODE]

• UA-FedRec: Untargeted Attack on Federated News Recommendation. [PUB] [PDF] [CODE]

• International Workshop on Federated Learning for Distributed Data Mining. [PUB] [PAGE]

• Is Normalization Indispensable for Multi-domain Federated Learning?. [PUB]

• Distributed Personalized Empirical Risk Minimization. [PUB]

• Once-for-All Federated Learning: Learning From and Deploying to Heterogeneous Clients. [PUB]

• SparseVFL: Communication-Efficient Vertical Federated Learning Based on Sparsification of Embeddings and Gradients. [PUB]

• Optimization of User Resources in Federated Learning for Urban Sensing Applications. [PUB]

• FedLEGO: Enabling Heterogenous Model Cooperation via Brick Reassembly in Federated Learning. [PUB]

• Federated Graph Analytics with Differential Privacy. [PUB]

• Scaling Distributed Multi-task Reinforcement Learning with Experience Sharing. [PUB]

• Uncertainty Quantification in Federated Learning for Heterogeneous Health Data. [PUB]

• A Systematic Evaluation of Federated Learning on Biomedical Natural Language Processing. [PUB]

• Taming Heterogeneity to Deal with Test-Time Shift in Federated Learning. [PUB]

• Federated Blood Supply Chain Demand Forecasting: A Case Study. [PUB]

• Stochastic Clustered Federated Learning. [PUB]

• A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection. [PUB]

• Exploring the Efficacy of Data-Decoupled Federated Learning for Image Classification and Medical Imaging Analysis. [PUB]

• FedNoisy: A Federated Noisy Label Learning Benchmark. [PUB]

• Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging. [PUB]

• Federated learning for competing risk analysis in healthcare. [PUB]

• Federated Threat Detection for Smart Home IoT rules. [PUB]

• A Collaborative Transfer Learning Framework for Cross-domain Recommendation. [PUB]

• Communication Efficient and Differentially Private Logistic Regression under the Distributed Setting. [PUB]

• Communication Efficient Distributed Newton Method with Fast Convergence Rates. [PUB]

WSDM

• Federated Unlearning for On-Device Recommendation. [PUB] [PDF]
• 4th Crowd Science Workshop - CANDLE: Collaboration of Humans and Learning Algorithms for Data Labeling. [PUB]

2022

KDD

• FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Platform for Federated Graph Learning :fire:. [PUB] [PDF] [CODE]
• Collaboration Equilibrium in Federated Learning. [PUB] [PDF] [CODE]
• Connected Low-Loss Subspace Learning for a Personalization in Federated Learning. [PUB] [PDF] [CODE]
• FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks. [PUB]
• Communication-Efficient Robust Federated Learning with Noisy Labels. [PUB] [PDF]
• FLDetector: Detecting Malicious Clients in Federated Learning via Checking Model-Updates Consistency. [PUB] [PDF] [CODE]
• Practical Lossless Federated Singular Vector Decomposition Over Billion-Scale Data. [PUB] [PDF] [CODE]
• FedWalk: Communication Efficient Federated Unsupervised Node Embedding with Differential Privacy. [PUB] [PDF]
• Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch. [PUB] [PDF] [解读]
• Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks. [PUB] [PDF]
• No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices. [PUB] [PDF]
• FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling. [PUB] [PDF] [CODE]
• A Practical Introduction to Federated Learning. [PUB]
• Connecting Low-Loss Subspace for Personalized Federated Learning. [PUB]
• FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning. [PUB] [CODE]
• FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients. [PUB]
• Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning. [PUB]

WSDM

• PipAttack: Poisoning Federated Recommender Systems for Manipulating Item Promotion. [PUB] [PDF]
• Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning. [PUB]

2021

KDD

• Fed2: Feature-Aligned Federated Learning. [PUB] [PDF]
• FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. [PUB] [CODE]
• Federated Adversarial Debiasing for Fair and Trasnferable Representations. [PUB] [PAGE] [CODE] [SLIDE]
• Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling. [PUB] [CODE] [解读]
• AsySQN: Faster Vertical Federated Learning Algorithms with Better Computation Resource Utilization. [PUB] [PDF]
• FLOP: Federated Learning on Medical Datasets using Partial Networks. [PUB] [PDF] [CODE]
• Federated Adversarial Debiasing for Fair and Transferable Representations. [PUB] [CODE]
• Towards Fair Federated Learning. [PUB]
• Device-Cloud Collaborative Learning for Recommendation. [PUB]

WSDM

• A Practical Federated Learning Framework for Small Number of Stakeholders. [PUB] [CODE]
• Federated Deep Knowledge Tracing. [PUB] [CODE]

2020

KDD

• FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. [PUB] [VIDEO]
• Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. [PUB] [PDF] [VIDEO]

2019

kdd

• A Collaborative Learning Framework to Tag Refinement for Points of Interest. [PUB]
• FDML: A Collaborative Machine Learning Framework for Distributed Features. [PUB]

WSDM

• Federated Online Learning to Rank with Evolution Strategies. [PUB] [CODE]

2018

kdd

• Collaborative Deep Metric Learning for Video Understanding. [PUB]
• Multi-label Learning with Highly Incomplete Data via Collaborative Embedding. [PUB]

wsdm

• Robust Transfer Learning for Cross-domain Collaborative Filtering Using Multiple Rating Patterns Approximation. [PUB]

2017

kdd

• Federated Tensor Factorization for Computational Phenotyping. [PUB]
• Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation. [PUB]
• Communication-Efficient Distributed Block Minimization for Nonlinear Kernel Machines. [PUB]

wsdm

• Representation Learning with Pair-wise Constraints for Collaborative Ranking. [PUB]

2016

kdd

• Communication Efficient Distributed Kernel Principal Component Analysis. [PUB]

2015

kdd

• Collaborative Deep Learning for Recommender Systems. [PUB]

2014

kdd

• Active collaborative permutation learning. [PUB]

2012

kdd

• Learning binary codes for collaborative filtering. [PUB]

wsdm

• Beyond ten blue links: enabling user click modeling in federated web search. [PUB]

2011

wsdm

• On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals. [PUB]

2026

NDSS

• A Unified Defense Framework Against Membership Inference in Federated Learning via Distillation and Contribution-Aware Aggregation. [PUB]
• Entente: Cross-silo Intrusion Detection on Network Log Graphs with Federated Learning. [PUB]
• ZKSL: Verifiable and Efficient Split Federated Learning via Asynchronous Zero-Knowledge Proofs. [PUB]
• SVDefense: Effective Defense against Gradient Inversion Attacks via Singular Value Decomposition. [PUB]

2025

CCS

• Armadillo: Robust Single-Server Secure Aggregation for Federated Learning with Input Validation. [PUB]
• FilterFL: Knowledge Filtering-based Data-Free Backdoor Defense for Federated Learning. [PUB]
• Harnessing Sparsification in Federated Learning: A Secure, Efficient, and Differentially Private Realization. [PUB]
• On Hyperparameters and Backdoor-Resistance in Horizontal Federated Learning. [PUB]
• Poster: Adaptive Gradient Clipping with Personalized Differential Privacy for Heterogeneous Federated Learning. [PUB]
• Secure Noise Sampling for Differentially Private Collaborative Learning. [PUB]

USENIX Security

• DP-BREM: Differentially-Private and Byzantine-Robust Federated Learning with Client Momentum. [PUB]
• FastLloyd: Federated, Accurate, Secure, and Tunable k-Means Clustering with Differential Privacy. [PUB]
• From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learning. [PUB]
• PoiSAFL: Scalable Poisoning Attack Framework to Byzantine-resilient Semi-asynchronous Federated Learning. [PUB]
• Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning. [PUB]
• SoK: Gradient Inversion Attacks in Federated Learning. [PUB]
• SoK: On Gradient Leakage in Federated Learning. [PUB]
• From Purity to Peril: Backdooring Merged Models From "Harmless" Benign Components. [PUB]

S&P

• Not All Edges are Equally Robust: Evaluating the Robustness of Ranking-Based Federated Learning. [PUB]
• Practical Poisoning Attacks with Limited Byzantine Clients in Clustered Federated Learning. [PUB]
• An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models. [PUB]
• Privacy-Preserving Mutual Authentication Protocol for Federated Learning in Intelligent Transportation Systems. [PUB]
• FedTilt: Towards Multi-Level Fairness-Preserving and Robust Federated Learning. [PUB]
• Enhancing Jailbreak Resistance in Large Language Models Using Model Merge. [PUB]
• On the Conflict Between Robustness and Learning in Collaborative Machine Learning. [PUB]

NDSS

• Privacy-Preserving Data Deduplication for Enhancing Federated Learning of Language Models. [PUB]
• Scale-MIA: A Scalable Model Inversion Attack against Secure Federated Learning via Latent Space Reconstruction. [PUB] [CODE]
• URVFL: Undetectable Data Reconstruction Attack on Vertical Federated Learning. [PUB] [CODE]
• RAIFLE: Reconstruction Attacks on Interaction-based Federated Learning with Adversarial Data Manipulation. [PUB] [CODE]
• CENSOR: Defense Against Gradient Inversion via Orthogonal Subspace Bayesian Sampling. [PUB]

2024

USENIX Security

• ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning. [PUB]
• BackdoorIndicator: Leveraging OOD Data for Proactive Backdoor Detection in Federated Learning. [PUB]
• Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach. [PUB]
• Efficient Privacy Auditing in Federated Learning. [PUB]
• FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning. [PUB]
• Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning. [PUB]
• Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning. [PUB]
• Accelerating Secure Collaborative Machine Learning with Protocol-Aware RDMA. [PUB]

CCS

• Byzantine-Robust Decentralized Federated Learning. [PUB]
• Not One Less: Exploring Interplay between User Profiles and Items in Untargeted Attacks against Federated Recommendation. [PUB]
• Cross-silo Federated Learning with Record-level Personalized Differential Privacy. [PUB]
• Samplable Anonymous Aggregation for Private Federated Data Analysis. [PUB]
• Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy. [PUB]
• Distributed Backdoor Attacks on Federated Graph Learning and Certified Defenses. [PUB] [CODE]
• Two-Tier Data Packing in RLWE-based Homomorphic Encryption for Secure Federated Learning. [PUB]
• Poster: Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling. [PUB]
• Poster: End-to-End Privacy-Preserving Vertical Federated Learning using Private Cross-Organizational Data Collaboration. [PUB]
• BadMerging: Backdoor Attacks Against Model Merging. [PUB] [CODE]
• CoGNN: Towards Secure and Efficient Collaborative Graph Learning. [PUB]
• Uncovering Gradient Inversion Risks in Practical Language Model Training. [PUB]

NDSS

• FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting. [PUB]
• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning. [PUB]
• Automatic Adversarial Adaption for Stealthy Poisoning Attacks in Federated Learning. [PUB]
• CrowdGuard: Federated Backdoor Detection in Federated Learning. [PUB]
• Pencil: Private and Extensible Collaborative Learning without the Non-Colluding Assumption. [PUB]

S&P

• Protecting Label Distribution in Cross-Silo Federated Learning. [PUB]

• FLShield: A Validation Based Federated Learning Framework to Defend Against Poisoning Attacks. [PUB]

• BadVFL: Backdoor Attacks in Vertical Federated Learning. [PUB]

• SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks. [PUB]

• Loki: Large-scale Data Reconstruction Attack against Federated Learning through Model Manipulation. [PUB]

• LayerDBA: Circumventing Similarity-Based Defenses in Federated Learning. [PUB]

• Poster: Towards Privacy-Preserving Federated Recommendation via Synthetic Interactions. [PUB]

• A Performance Analysis for Confidential Federated Learning. [PUB]

2023

CCS

• Turning Privacy-preserving Mechanisms against Federated Learning. [PUB] [PDF]
• MESAS: Poisoning Defense for Federated Learning Resilient against Adaptive Attackers. [PUB]
• martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture. [PUB] [PDF] [CODE]
• Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks. [PUB] [PDF]
• Poster: Verifiable Data Valuation with Strong Fairness in Horizontal Federated Learning. [PUB]
• Poster: Bridging Trust Gaps: Data Usage Transparency in Federated Data Ecosystems. [PUB]
• Turning Privacy-preserving Mechanisms against Federated Learning. [PUB] [PDF] [CODE]

USENIX Security

• Every Vote Counts: Ranking-Based Training of Federated Learning to Resist Poisoning Attacks. [PUB] [PDF]
• PrivateFL: Accurate, Differentially Private Federated Learning via Personalized Data Transformation. [PUB] [CODE]
• Gradient Obfuscation Gives a False Sense of Security in Federated Learning. [PUB] [PDF] [CODE]
• FedVal: Different good or different bad in federated learning. [PUB] [PDF] [CODE]
• HOLMES: Efficient Distribution Testing for Secure Collaborative Learning. [PUB]

NDSS

• Securing Federated Sensitive Topic Classification against Poisoning Attacks. [PUB] [PDF] [CODE]
• PPA: Preference Profiling Attack Against Federated Learning. [PUB] [PDF]

S&P

• FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information. [PUB] [PDF]

• Scalable and Privacy-Preserving Federated Principal Component Analysis. [PUB] [PDF]

• BayBFed: Bayesian Backdoor Defense for Federated Learning. [PUB] [PDF]

• 3DFed: Adaptive and Extensible Framework for Covert Backdoor Attack in Federated Learning. [PUB] [CODE]

• RoFL: Robustness of Secure Federated Learning. [PUB] [PDF] [CODE]

• Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning. [PUB] [CODE]

• ELSA: Secure Aggregation for Federated Learning with Malicious Actors.

• Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy. [PUB] [PDF]

• SafeFL: MPC-friendly Framework for Private and Robust Federated Learning. [PUB]

• On the Pitfalls of Security Evaluation of Robust Federated Learning. [PUB]

• ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems. [PUB]

• ELSA: Secure Aggregation for Federated Learning with Malicious Actors. [PUB]

2022

CCS

• CERBERUS: Exploring Federated Prediction of Security Events. [PUB] [PDF]
• EIFFeL: Ensuring Integrity for Federated Learning. [PUB] [PDF]
• Eluding Secure Aggregation in Federated Learning via Model Inconsistency. [PUB] [PDF] [CODE]
• Federated Boosted Decision Trees with Differential Privacy. [PUB] [PDF] [CODE]

S&P

• Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning. [PUB] [VIDEO]
• SNARKBlock: Federated Anonymous Blocklisting from Hidden Common Input Aggregate Proofs. [PUB]

USENIX Security

• SIMC: ML Inference Secure Against Malicious Clients at Semi-Honest Cost. [PUB] [PDF] [CODE] [VIDEO] [SUPP]
• Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors. [PUB] [SLIDE] [VIDEO]
• Label Inference Attacks Against Vertical Federated Learning. [PUB] [SLIDE] [CODE] [VIDEO]
• FLAME: Taming Backdoors in Federated Learning. [PUB] [SLIDE] [PDF] [VIDEO]

NDSS

• Local and Central Differential Privacy for Robustness and Privacy in Federated Learning. [PUB] [PDF] [VIDEO] [UC.]
• Interpretable Federated Transformer Log Learning for Cloud Threat Forensics. [PUB] [VIDEO] [UC.]
• FedCRI: Federated Mobile Cyber-Risk Intelligence. [PUB] [VIDEO]
• DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection. [PUB] [PDF] [VIDEO]

2021

CCS

• Private Hierarchical Clustering in Federated Networks. [PUB] [PDF]

NDSS

• FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping. [PUB] [PDF] [CODE] [VIDEO] [SLIDE]
• POSEIDON: Privacy-Preserving Federated Neural Network Learning. [PUB] [VIDEO]
• Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning. [PUB] [CODE] [VIDEO]

s&p

• SAFELearn: Secure Aggregation for private FEderated Learning. [PUB]

S&P Workshop

• SAFELearn: Secure Aggregation for private FEderated Learning. [PUB]

usenix security

• Cerebro: A Platform for Multi-Party Cryptographic Collaborative Learning. [PUB]

2020

ndss

• Strong Authentication without Temper-Resistant Hardware and Application to Federated Identities. [PUB]

s&p

• The Value of Collaboration in Convex Machine Learning with Differential Privacy. [PUB]

USENIX Security

• Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. [PUB] [PDF] [CODE] [VIDEO] [SLIDE]

2019

CCS

• A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain. [PUB]
• Poster: A Reliable and Accountable Privacy-Preserving Federated Learning Framework using the Blockchain. [PUB]

S&P

• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning :fire:. [PUB] [VIDEO] [SLIDE] [CODE]
• IOTFLA : A Secured and Privacy-Preserving Smart Home Architecture Implementing Federated Learning. [PUB]
• Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. [PUB]
• Exploiting Unintended Feature Leakage in Collaborative Learning. [PUB]

usenix security

• Triton: A Software-Reconfigurable Federated Avionics Testbed. [PUB]

2018

ccs

• The Price of Privacy in Collaborative Learning. [PUB]

2017

CCS

• Practical Secure Aggregation for Privacy Preserving Machine Learning. [PUB] [PDF] [解读] [UC.] [UC]
• Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. [PUB]

2015

s&p

• Privacy by Design in Federated Identity Management. [PUB]

2014

ndss

• Hardening Persona - Improving Federated Web Login. [PUB]

2026

ijcv

• Collaborative Temporal Consistency Learning for Point-supervised Natural Language Video Localization. [PUB]
• CoSurfGS: 3D Surface Gaussian Splatting with Collaborative Distributed Learning for Large-scale Scene Reconstruction. [PUB]

2025

iccv

• A Framework for Double-Blind Federated Adaptation of Foundation Models. [PUB] [CODE]
• Class-Wise Federated Averaging for Efficient Personalization. [PUB]
• Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing. [PUB] [CODE]
• Cooperative Pseudo Labeling for Unsupervised Federated Classification. [PUB] [CODE]
• EFTViT: Efficient Federated Training of Vision Transformers with Masked Images on Resource-Constrained Clients. [PUB]
• FDPT: Federated Discrete Prompt Tuning for Black-Box Visual-Language Models. [PUB]
• FedAGC: Federated Continual Learning with Asymmetric Gradient Correction. [PUB]
• FedDifRC: Unlocking the Potential of Text-to-Image Diffusion Models in Heterogeneous Federated Learning. [PUB] [CODE]
• Federated Continual Instruction Tuning. [PUB] [CODE]
• Federated Continuous Category Discovery and Learning. [PUB]
• Federated Domain Generalization with Domain-Specific Soft Prompts Generation. [PUB]
• Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data. [PUB]
• Federated Representation Angle Learning. [PUB]
• FedMeNF: Privacy-Preserving Federated Meta-Learning for Neural Fields. [PUB]
• FedMVP: Federated Multimodal Visual Prompt Tuning for Vision-Language Models. [PUB] [CODE]
• FedPall: Prototype-Based Adversarial and Collaborative Learning for Federated Learning with Feature Drift. [PUB] [CODE]
• FedVLA: Federated Vision-Language-Action Learning with Dual Gating Mixture-of-Experts for Robotic Manipulation. [PUB]
• FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization. [PUB]
• FedXDS: Leveraging Model Attribution Methods to Counteract Data Heterogeneity in Federated Learning. [PUB]
• Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning. [PUB]
• FLSeg: Enhancing Privacy and Robustness in Federated Learning under Heterogeneous Data via Model Segmentation. [PUB]
• Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation. [PUB] [CODE]
• Geminio: Language-Guided Gradient Inversion Attacks in Federated Learning. [PUB]
• Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning. [PUB] [CODE]
• LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement. [PUB]
• Neural Architecture Search Driven by Locally Guided Diffusion for Personalized Federated Learning. [PUB]
• Personalized Federated Learning Under Local Supervision. [PUB]
• Sibai: A Few-Shot Meta-Classifier for Poisoning Detection in Federated Learning. [PUB]
• Soft Separation and Distillation: Toward Global Uniformity in Federated Unsupervised Learning. [PUB]
• Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-Wise Gradient Alignment. [PUB]
• Task-Aware Prompt Gradient Projection for Parameter-Efficient Tuning Federated Class-Incremental Learning. [PUB]
• Tensor-Aggregated LoRA in Federated Fine-Tuning. [PUB]
• Towards Privacy-preserved Pre-training of Remote Sensing Foundation Models with Federated Mutual-Guidance Learning. [PUB]
• You are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-Tailed Data. [PUB] [CODE]
• COME: Dual Structure-Semantic Learning with Collaborative MOE for Universal Lesion Detection Across Heterogeneous Ultrasound Datasets. [PUB]
• Disrupting Model Merging: A Parameter-Level Defense without Sacrificing Accuracy. [PUB] [CODE]
• Free-Merging: Fourier Transform for Efficient Model Merging. [PUB] [CODE]
• FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization. [PUB]
• Task Vector Quantization for Memory-Efficient Model Merging. [PUB]
• Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning. [PUB] [CODE]

MM

• Client-Server Co-design with Multi-modal Codebooks Makes Better and Faster Federate Knowledge Sharing. [PUB]
• Consistency of Local and Global Flatness for Federated Learning. [PUB] [CODE]
• Discovering Maximum Frequency Consensus: Lightweight Federated Learning for Medical Image Segmentation. [PUB]
• Diverse and Public Features Cooperation via Gradient Rectification for Federated Prompt Learning. [PUB]
• DualFPT: Handling Data Heterogeneity in Federated Prompt Tuning from both Generalized and Personalized Perspective. [PUB]
• DynFed: Adaptive Federated Learning via Quantization-Aware Knowledge Distillation. [PUB]
• FeatShield: Isolating Malicious Feature Extractors for Backdoor-Robust Federated Learning. [PUB]
• FedAPT: Federated Adversarial Prompt Tuning for Vision-Language Models. [PUB]
• FedBAP: Backdoor Defense via Benign Adversarial Perturbation in Federated Learning. [PUB]
• FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning. [PUB]
• Federated Incomplete Multi-view Clustering with Individual Structure Preservation and Central Representation Tensorization. [PUB] [CODE]
• FedRog: Robust Federated Graph Classification for Strong Heterogeneity and High-Noise Scenarios. [PUB]
• FORGET ME: Federated Unlearning for Face Generation Models. [PUB] [CODE]
• Multi-Width Neural Network-Assisted Hierarchical Federated Learning in Heterogeneous Cloud-Edge-Device Computing. [PUB]
• Positive Style Accumulation: A Style Screening and Continuous Utilization Framework for Federated DG-ReID. [PUB]
• PriCAF: Privacy-Preserving Contribution Assessment in Federated Learning Before Model Training. [PUB]
• Device-Cloud Collaborative Learning Framework for Efficient Unknown Object Detection. [PUB]
• Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement Learning. [PUB]
• Multi-view Collaborative Representation Learning from Noisy Labels for VHR Imagery Classification. [PUB]
• Outlier-Aware Model Merging for Efficient Multitask Inference. [PUB]
• Spatial-Frequency Mamba Collaborative Learning Network for Infrared Small Target Detection. [PUB]
• Task Arithmetic in Trust Region: A Training-Free Model Merging Approach to Navigate Knowledge Conflicts. [PUB]
• Tractography-Guided Dual-Label Collaborative Learning for Multi-Modal Cranial Nerves Parcellation. [PUB]

CVPR

• Federated Learning with Domain Shift Eraser. [PUB]
• Beyond Local Sharpness: Communication-Efficient Global Sharpness-aware Minimization for Federated Learning. [PUB] [CODE]
• FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models. [PUB] [CODE]
• FedCS: Coreset Selection for Federated Learning. [PUB]
• AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models. [PUB] [CODE]
• NoT: Federated Unlearning via Weight Negation. [PUB]
• Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution. [PUB]
• Infighting in the Dark: Multi-Label Backdoor Attack in Federated Learning. [PUB]
• Mind the Gap: Confidence Discrepancy Can Guide Federated Semi-Supervised Learning Across Pseudo-Mismatch. [PUB] [CODE]
• Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning. [PUB] [CODE]
• HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving. [PUB] [COCE]
• F^3OCUS - Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics. [PUB] [PAGE]
• FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors. [PUB]
• FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity. [PUB] [CODE]
• Population Normalization for Federated Learning. [PUB]
• Model Poisoning Attacks to Federated Learning via Multi-Round Consistency. [PUB] [CODE]
• dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis. [PUB]
• Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection. [PUB] [CODE]
• A Simple Data Augmentation for Feature Distribution Skewed Federated Learning. [PUB] [CODE]
• Handling Spatial-Temporal Data Heterogeneity for Federated Continual Learning via Tail Anchor. [PUB]
• Subspace Constraint and Contribution Estimation for Heterogeneous Federated Learning. [PUB] [CODE]
• pFedMxF: Personalized Federated Class-Incremental Learning with Mixture of Frequency Aggregation. [PUB]
• FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning. [PUB]
• Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter. [PUB]
• FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning. [PUB] [CODE]
• Patient-Level Anatomy Meets Scanning-Level Physics: Personalized Federated Low-Dose CT Denoising Empowered by Large Language Model. [PUB]
• FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning. [PUB] [CODE]
• AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization. [PUB]
• Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning. [PUB]
• Embracing Collaboration Over Competition: Condensing Multiple Prompts for Visual In-Context Learning. [PUB] [CODE]
• Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning. [PUB] [CODE]
• How to Merge Your Multimodal Models Over Time?. [PUB]
• Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation. [PUB] [CODE]
• Less is More: Efficient Model Merging with Binary Task Switch. [PUB]
• Libra-Merging: Importance-redundancy and Pruning-merging Trade-off for Acceleration Plug-in in Large Vision-Language Model. [PUB] [CODE]
• OnlineAnySeg: Online Zero-Shot 3D Segmentation by Visual Foundation Model Guided 2D Mask Merging. [PUB]
• PLeaS - Merging Models with Permutations and Least Squares. [PUB]
• PromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval. [PUB] [CODE]
• Task Singular Vectors: Reducing Task Interference in Model Merging. [PUB]
• Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning. [PUB]
• Weakly Supervised Temporal Action Localization via Dual-Prior Collaborative Learning Guided by Multimodal Large Language Models. [PUB]

IJCV

• Relation-Guided Versatile Regularization for Federated Semi-Supervised Learning. [PUB]
• Achieving Procedure-Aware Instructional Video Correlation Learning Under Weak Supervision from a Collaborative Perspective. [PUB]
• HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning. [PUB] [CODE]
• Semantic-Aligned Learning with Collaborative Refinement for Unsupervised VI-ReID. [PUB] [CODE]

2024

MM

• DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations. [PUB] [CODE]
• One-shot-but-not-degraded Federated Learning. [PUB] [CODE]
• Overcoming Spatial-Temporal Catastrophic Forgetting for Federated Class-Incremental Learning. [PUB] [CODE]
• FedDEO: Description-Enhanced One-Shot Federated Learning with Diffusion Models. [PUB]
• Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-rank Decomposition. [PUB] [CODE]
• CoAst: Validation-Free Contribution Assessment for Federated Learning based on Cross-Round Valuation. [PUB]
• Spatio-temporal Heterogeneous Federated Learning for Time Series Classification with Multi-view Orthogonal Training. [PUB]
• FedEvalFair: A Privacy-Preserving and Statistically Grounded Federated Fairness Evaluation Framework. [PUB]
• One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity. [PUB] [CODE]
• FedSLS: Exploring Federated Aggregation in Saliency Latent Space. [PUB]
• Cluster-driven Personalized Federated Recommendation with Interest-aware Graph Convolution Network for Multimedia. [PUB]
• FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning. [PUB]
• Federated Morozov Regularization for Shortcut Learning in Privacy Preserving Learning with Watermarked Image Data. [PUB]
• Cross-Modal Meta Consensus for Heterogeneous Federated Learning. [PUB]
• Masked Random Noise for Communication-Efficient Federated Learning. [PUB]
• Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations. [PUB]
• Adaptive Hierarchical Aggregation for Federated Object Detection. [PUB]
• FedCAFE: Federated Cross-Modal Hashing with Adaptive Feature Enhancement. [PUB]
• Federated Fuzzy C-means with Schatten-p Norm Minimization. [PUB]
• Towards Effective Federated Graph Anomaly Detection via Self-boosted Knowledge Distillation. [PUB]
• CoPL: Parameter-Efficient Collaborative Prompt Learning for Audio-Visual Tasks. [PUB]

IJCV

• Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification. [PUB] [CODE]

ECCV

• SKYMASK: Attack-Agnostic Robust Federated Learning with Fine-Grained Learnable Masks. [PUB] [CODE]
• FedHide: Federated Learning by Hiding in the Neighbors. [PUB]
• FedVAD: Enhancing Federated Video Anomaly Detection with GPT-Driven Semantic Distillation. [PUB]
• FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients. [PUB]
• Pick-a-Back: Selective Device-to-Device Knowledge Transfer in Federated Continual Learning. [PUB]
• Federated Learning with Local Openset Noisy Labels. [PUB]
• FedTSA: A Cluster-Based Two-Stage Aggregation Method for Model-Heterogeneous Federated Learning. [PUB]
• Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality Selection. [PUB]
• BAFFLE: A Baseline of Backpropagation-Free Federated Learning. [PUB] [CODE]
• PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning. [PUB] [CODE]
• Fisher Calibration for Backdoor-Robust Heterogeneous Federated Learning. [PUB]
• Unlocking the Potential of Federated Learning: The Symphony of Dataset Distillation via Deep Generative Latents. [PUB]
• FedHARM: Harmonizing Model Architectural Diversity in Federated Learning. [PUB]
• SuperFedNAS: Cost-Efficient Federated Neural Architecture Search for On-device Inference. [PUB]
• Personalized Federated Domain-Incremental Learning Based on Adaptive Knowledge Matching. [PUB]
• Diffusion-Driven Data Replay: A Novel Approach to Combat Forgetting in Federated Class Continual Learning. [PUB]
• Towards Multi-modal Transformers in Federated Learning. [PUB]
• Local and Global Flatness for Federated Domain Generalization. [PUB]
• Feature Diversification and Adaptation for Federated Domain Generalization. [PUB]
• PFEDEDIT: Personalized Federated Learning via Automated Model Editing. [PUB]
• CoLeaF: A Contrastive-Collaborative Learning Framework for Weakly Supervised Audio-Visual Video Parsing. [PUB]
• Diffusion Soup: Model Merging for Text-to-Image Diffusion Models. [PUB]
• MAGMAX: Leveraging Model Merging for Seamless Continual Learning. [PUB]
• Model Breadcrumbs: Scaling Multi-task Model Merging with Sparse Masks. [PUB]
• Multi-branch Collaborative Learning Network for 3D Visual Grounding. [PUB]
• Training-Free Model Merging for Multi-target Domain Adaptation. [PUB]

CVPR

• FedHCA2: Towards Hetero-Client Federated Multi-Task Learning. [PUB] [SUPP] [PDF] [CODE]
• Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity. [PUB] [PDF] [CODE]
• Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain Shifts. [PUB] [SUPP] [PDF] [CODE]
• FedMef: Towards Memory-efficient Federated Dynamic Pruning. [PUB] [SUPP] [PDF]
• Communication-Efficient Federated Learning with Accelerated Client Gradient. [PUB] [SUPP] [PDF] [CODE]
• Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space. [PUB] [SUPP] [CODE]
• Adaptive Hyper-graph Aggregation for Modality-Agnostic Federated Learning. [PUB] [SUPP] [CODE]
• Towards Efficient Replay in Federated Incremental Learning. [PUB] [SUPP] [PDF]
• Mixed-Precision Quantization for Federated Learning on Resource-Constrained Heterogeneous Devices. [PUB] [SUPP] [PDF]
• Data Valuation and Detections in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Decentralized Directed Collaboration for Personalized Federated Learning. [PUB] [SUPP] [PDF]
• Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Global and Local Prompts Cooperation via Optimal Transport for Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Rethinking the Representation in Federated Unsupervised Learning with Non-IID Data. [PUB] [SUPP] [PDF] [CODE]
• Relaxed Contrastive Learning for Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning. [PUB] [SUPP] [PDF] [VIDEO]
• Traceable Federated Continual Learning. [PUB] [SUPP] [CODE]
• Federated Online Adaptation for Deep Stereo. [PUB] [SUPP] [PDF] [CODE] [PAGE] [VIDEO]
• Federated Generalized Category Discovery. [PUB] [SUPP] [PDF] [CODE]
• Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization. [PUB] [SUPP] [PDF] [CODE]
• Text-Enhanced Data-free Approach for Federated Class-Incremental Learning. [PUB] [SUPP] [PDF] [CODE]
• PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees. [PUB] [SUPP] [PDF] [CODE]
• FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning. [PUB] [SUPP] [PDF] [CODE]
• FedUV: Uniformity and Variance for Heterogeneous Federated Learning. [PUB] [SUPP] [PDF]
• FedAS: Bridging Inconsistency in Personalized Federated Learning. [PUB] [CODE]
• FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning. [PUB] [SUPP] [PDF] [CODE]
• Device-Wise Federated Network Pruning. [PUB] [SUPP]
• Byzantine-robust Decentralized Federated Learning via Dual-domain Clustering and Trust Bootstrapping. [PUB] [SUPP]
• DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning. [PUB] [SUPP] [PDF]
• An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning. [PUB] [SUPP] [PDF] [CODE] [POSTER] [SLIDES]
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity. [PUB] [SUPP] [PDF]
• FLHetBench: Benchmarking Device and State Heterogeneity in Federated Learning. [PUB] [SUPP] [CODE] [PAGE] [POSTER] [VIDEO]
• Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning. [PUB]
• Revamping Federated Learning Security from a Defender's Perspective: A Unified Defense with Homomorphic Encrypted Data Space. [PUB]
• CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion. [PUB] [CODE]
• Cloud-Device Collaborative Learning for Multimodal Large Language Models. [PUB]
• DIMAT: Decentralized Iterative Merging-And-Training for Deep Learning Models. [PUB]
• Dual-Enhanced Coreset Selection with Class-Wise Collaboration for Online Blurry Class Incremental Learning. [PUB]
• Improving Plasticity in Online Continual Learning via Collaborative Learning. [PUB] [CODE]
• NoiseCollage: A Layout-Aware Text-to-Image Diffusion Model Based on Noise Cropping and Merging. [PUB] [CODE]
• Shallow-Deep Collaborative Learning for Unsupervised Visible-Infrared Person Re-Identification. [PUB]
• Training-Free Pretrained Model Merging. [PUB] [CODE]
• Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New Baseline. [PUB]