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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 91–100 of 311 papers

TitleStatusHype
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Federated Face Forgery Detection Learning with Personalized RepresentationCode0
Regularizing and Aggregating Clients with Class Distribution for Personalized Federated LearningCode0
Decentralized Personalized Federated Learning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
Federated Representation Learning in the Under-Parameterized RegimeCode0
Sheaf HyperNetworks for Personalized Federated Learning—0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity—0
FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP OptimizationCode0
Decentralized Directed Collaboration for Personalized Federated Learning—0
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