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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 271–280 of 311 papers

TitleStatusHype
Group privacy for personalized federated learning—0
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning—0
Hierarchical Over-the-Air FedGradNorm—0
Hierarchical Personalized Federated Learning Over Massive Mobile Edge Computing Networks—0
How To Prevent the Poor Performance Clients for Personalized Federated Learning?—0
Incentivizing Inclusive Contributions in Model Sharing Markets—0
Inference-Time Personalized Federated Learning—0
On-Demand Unlabeled Personalized Federated Learning—0
Influence-oriented Personalized Federated Learning—0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance—0
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