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Personalized Federated Learning

The federated learning setup presents numerous challenges including data heterogeneity (differences in data distribution), device heterogeneity (in terms of computation capabilities, network connection, etc.), and communication efficiency. Especially data heterogeneity makes it hard to learn a single shared global model that applies to all clients. To overcome these issues, Personalized Federated Learning (PFL) aims to personalize the global model for each client in the federation.

Papers

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Showing 251–300 of 311 papers

TitleStatusHype
Spectral Co-Distillation for Personalized Federated LearningCode0
Learn What You Need in Personalized Federated LearningCode0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax GuaranteesCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language ModelsCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated LearningCode0
Straggler-Resilient Personalized Federated LearningCode0
Revisiting Personalized Federated Learning: Robustness Against Backdoor AttacksCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
pFL-Bench: A Comprehensive Benchmark for Personalized Federated LearningCode0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Personalized Federated Learning via StackingCode0
Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning ApproachCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
Flow: Per-Instance Personalized Federated Learning Through Dynamic RoutingCode0
DAMe: Personalized Federated Social Event Detection with Dual Aggregation MechanismCode0
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter UpdateCode0
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal TransportCode0
FedAH: Aggregated Head for Personalized Federated LearningCode0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
Federated Learning of Dynamic Bayesian Network via Continuous Optimization from Time Series DataCode0
Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-TuningCode0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based ClusteringCode0
Personalized Federated Learning with Multiple Known ClustersCode0
Personalized Federated Learning with Server-Side InformationCode0
Personalized Multi-tier Federated LearningCode0
Privacy-preserving patient clustering for personalized federated learningCode0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
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