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. 🔥. [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 🔥. [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 🔥. [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](https://d