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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 1–25 of 311 papers

TitleStatusHype
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and BenchmarkCode4
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated LearningCode4
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional PolicyCode4
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised LearningCode3
FedModule: A Modular Federated Learning FrameworkCode2
ZooPFL: Exploring Black-box Foundation Models for Personalized Federated LearningCode2
Adaptive Personalized Federated LearningCode2
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated LearningCode1
Optimizing Personalized Federated Learning through Adaptive Layer-Wise LearningCode1
Personalized Federated Learning via Feature Distribution AdaptationCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
Personalized Federated Continual Learning via Multi-granularity PromptCode1
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-TuningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
You Can Backdoor Personalized Federated LearningCode1
Personalized Federated Learning with Feature Alignment and Classifier CollaborationCode1
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
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