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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
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning—0
Cross-Fusion Rule for Personalized Federated Learning—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
Decentralized Personalized Federated Learning—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models—0
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