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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 141–150 of 311 papers

TitleStatusHype
Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data—0
FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning—0
Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks—0
Personalized Federated Learning via ADMM with Moreau Envelope—0
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Adaptive Test-Time Personalization for Federated LearningCode1
Contextual Stochastic Bilevel Optimization—0
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
PPFL: A Personalized Federated Learning Framework for Heterogeneous Population—0
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