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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 261–270 of 311 papers

TitleStatusHype
FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning—0
FedSheafHN: Personalized Federated Learning on Graph-structured Data—0
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification—0
FedSPD: A Soft-clustering Approach for Personalized Decentralized Federated Learning—0
FedSub: Introducing class-aware Subnetworks Fusion to Enhance Personalized Federated Learning in Ubiquitous Systems—0
Find Your Friends: Personalized Federated Learning with the Right Collaborators—0
Federated Asymptotics: a model to compare federated learning algorithms—0
Formal Logic Enabled Personalized Federated Learning Through Property Inference—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
Group Personalized Federated Learning—0
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