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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 131–140 of 311 papers

TitleStatusHype
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
FedMCSA: Personalized Federated Learning via Model Components Self-Attention—0
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