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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 171–180 of 311 papers

TitleStatusHype
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation—0
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks—0
Robust and Personalized Federated Learning with Spurious Features: an Adversarial Approach—0
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning—0
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning—0
Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity—0
Self-Aware Personalized Federated Learning—0
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning—0
Semi-Synchronous Personalized Federated Learning over Mobile Edge Networks—0
Sheaf HyperNetworks for Personalized Federated Learning—0
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