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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
An Optimal Transport Approach to Personalized Federated Learning—0
Straggler-Resilient Personalized Federated LearningCode0
Personalized Federated Learning with Server-Side InformationCode0
ActPerFL: Active Personalized Federated Learning—0
Personalized Federated Learning with Multiple Known ClustersCode0
Self-Aware Personalized Federated Learning—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
FedGradNorm: Personalized Federated Gradient-Normalized Multi-Task Learning—0
Personalized Federated Learning with Exact Stochastic Gradient Descent—0
PerFED-GAN: Personalized Federated Learning via Generative Adversarial Networks—0
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