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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 121–130 of 311 papers

TitleStatusHype
DP^2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization—0
TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based ClusteringCode0
Personalized Federated Learning Techniques: Empirical Analysis—0
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning—0
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
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