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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 41–50 of 311 papers

TitleStatusHype
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANsCode1
Efficient Personalized Federated Learning via Sparse Model-AdaptationCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Personalized Federated Learning with Adaptive Batchnorm for HealthcareCode1
Federated Multi-Task Learning under a Mixture of DistributionsCode1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank DecompositionCode1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical DatasetsCode1
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated LearningCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated LearningCode1
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