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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 181–190 of 311 papers

TitleStatusHype
Collaborative Chinese Text Recognition with Personalized Federated Learning—0
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Personalized Federated Learning under Mixture of DistributionsCode1
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
Personalized Federated Learning via Gradient Modulation for Heterogeneous Text Summarization—0
Federated Learning of Shareable Bases for Personalization-Friendly Image Classification—0
IP-FL: Incentivized and Personalized Federated Learning—0
Personalized Federated Learning with Local Attention—0
FedFTN: Personalized Federated Learning with Deep Feature Transformation Network for Multi-institutional Low-count PET DenoisingCode0
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
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