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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 221–230 of 311 papers

TitleStatusHype
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Personalized Federated Learning with Local Attention—0
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
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
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
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