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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 181–190 of 311 papers

TitleStatusHype
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Personalized Federated Learning with Contextual Modulation and Meta-LearningCode0
Personalized Federated Learning with Attention-based Client Selection—0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data HeterogeneityCode0
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks—0
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
pFedES: Model Heterogeneous Personalized Federated Learning with Feature Extractor Sharing—0
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