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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 251–260 of 311 papers

TitleStatusHype
Personalized Federated Learning with Communication Compression—0
Personalized Federated Learning with Attention-based Client Selection—0
Personalized Federated Learning with Adaptive Feature Aggregation and Knowledge Transfer—0
Personalized Federated Learning with Exact Stochastic Gradient Descent—0
Learn What You Need in Personalized Federated LearningCode0
Sparse Personalized Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Federated Representation Learning in the Under-Parameterized RegimeCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Personalized Federated Learning via Heterogeneous Modular NetworksCode0
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