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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 51–75 of 311 papers

TitleStatusHype
Personalized Federated Learning under Mixture of DistributionsCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Personalized Federated Learning With GraphCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
PFL-MoE: Personalized Federated Learning Based on Mixture of ExpertsCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Optimizing Personalized Federated Learning through Adaptive Layer-Wise LearningCode1
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
Learn What You Need in Personalized Federated LearningCode0
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant ClientsCode0
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