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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 191–200 of 311 papers

TitleStatusHype
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks—0
Visual Prompt Based Personalized Federated Learning—0
Fusion of Global and Local Knowledge for Personalized Federated LearningCode0
FedABC: Targeting Fair Competition in Personalized Federated Learning—0
PerAda: Parameter-Efficient Federated Learning Personalization with Generalization GuaranteesCode1
Cross-Fusion Rule for Personalized Federated Learning—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks—0
The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Personalized Semantics Excitation for Federated Image Classification—0
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