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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 276–300 of 311 papers

TitleStatusHype
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter UpdateCode0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated LearningCode0
Spectral Co-Distillation for Personalized Federated LearningCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax GuaranteesCode0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
Straggler-Resilient Personalized Federated LearningCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
An Equivalence Between Data Poisoning and Byzantine Gradient AttacksCode0
DAMe: Personalized Federated Social Event Detection with Dual Aggregation MechanismCode0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions 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
PGFed: Personalize Each Client's Global Objective for Federated LearningCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
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
Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression RecognitionCode0
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