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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 191–200 of 311 papers

TitleStatusHype
Personalized Federated Learning via ADMM with Moreau Envelope—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Contextual Stochastic Bilevel Optimization—0
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population—0
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—0
PFL-GAN: When Client Heterogeneity Meets Generative Models in Personalized Federated Learning—0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
UPFL: Unsupervised Personalized Federated Learning towards New ClientsCode0
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