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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
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
Asynchronous Personalized Federated Learning through Global Memorization—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
Federated Learning for Chronic Obstructive Pulmonary Disease Classification with Partial Personalized Attention Mechanism—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
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