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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 201–210 of 311 papers

TitleStatusHype
Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space—0
Privacy-preserving patient clustering for personalized federated learningCode0
Advances and Challenges in Meta-Learning: A Technical Review—0
Personalized Federated Learning via Amortized Bayesian Meta-Learning—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Provably Personalized and Robust Federated LearningCode0
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
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