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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 171–180 of 311 papers

TitleStatusHype
Personalized Federated Learning for Statistical Heterogeneity—0
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated LearningCode0
Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation—0
Spectral Co-Distillation for Personalized Federated LearningCode0
Viewport Prediction, Bitrate Selection, and Beamforming Design for THz-Enabled 360° Video Streaming—0
Personalized Over-the-Air Federated Learning with Personalized Reconfigurable Intelligent Surfaces—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed DataCode0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach—0
Learn What You Need in Personalized Federated LearningCode0
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