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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 71–80 of 311 papers

TitleStatusHype
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning—0
FedModule: A Modular Federated Learning FrameworkCode2
Personalized Federated Learning via Active Sampling—0
DAMe: Personalized Federated Social Event Detection with Dual Aggregation MechanismCode0
FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions—0
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts—0
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas—0
Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic SurgeryCode0
Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning—0
Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering—0
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