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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 71–80 of 311 papers

TitleStatusHype
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
Achieving Personalized Federated Learning with Sparse Local Models—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning—0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates—0
A Theorem of the Alternative for Personalized Federated Learning—0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—0
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