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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 231–240 of 311 papers

TitleStatusHype
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI SynthesisCode1
An Empirical Study of Personalized Federated LearningCode1
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
Personalized Federated Learning via Variational Bayesian InferenceCode1
Adaptive Expert Models for Personalization in Federated LearningCode0
Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT ImagingCode1
pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning—0
Group privacy for personalized federated learning—0
An Optimal Transport Approach to Personalized Federated Learning—0
Straggler-Resilient Personalized Federated LearningCode0
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