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 11–20 of 311 papers

Title	Date	Tasks	Status	Hype
Personalized Federated Learning via Feature Distribution Adaptation	Nov 1, 2024	Federated LearningNavigate	CodeCode Available	1
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition	Jun 28, 2024	Federated LearningGeneral Knowledge	CodeCode Available	1
Personalized Federated Continual Learning via Multi-granularity Prompt	Jun 27, 2024	Continual LearningFederated Learning	CodeCode Available	1
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning	Apr 3, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	1
FedAS: Bridging Inconsistency in Personalized Federated Learning	Jan 1, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning	Dec 14, 2023	Computational EfficiencyFederated Learning	CodeCode Available	1
Adaptive Test-Time Personalization for Federated Learning	Oct 28, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Unlocking the Potential of Prompt-Tuning in Bridging Generalized and Personalized Federated Learning	Oct 27, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets	Sep 28, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive Collaboration	Sep 20, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	1

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