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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 171–180 of 311 papers

TitleStatusHype
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Personalization Disentanglement for Federated Learning: An explainable perspective—0
Personalized Federated Domain Adaptation for Item-to-Item Recommendation—0
Partially Personalized Federated Learning: Breaking the Curse of Data Heterogeneity—0
Federated Neural Compression Under Heterogeneous Data—0
pFedSim: Similarity-Aware Model Aggregation Towards Personalized Federated Learning—0
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training—0
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
A novel parameter decoupling approach of personalized federated learning for image analysis—0
FedDWA: Personalized Federated Learning with Dynamic Weight AdjustmentCode0
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