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 101–110 of 311 papers

Title	Date	Tasks	Status	Score
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central Server	Mar 21, 2024	Federated LearningMulti-Task Learning	CodeCode Available	5
pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning	Feb 2, 2024	Federated LearningMixture-of-Experts	CodeCode Available	5
FedAli: Personalized Federated Learning with Aligned Prototypes through Optimal Transport	Nov 15, 2024	Activity RecognitionFederated Learning	CodeCode Available	5
Low-Resource Machine Translation through the Lens of Personalized Federated Learning	Jun 18, 2024	Federated LearningMachine Translation	CodeCode Available	5
FedAH: Aggregated Head for Personalized Federated Learning	Dec 2, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare	Apr 15, 2025	BenchmarkingDiagnostic	CodeCode Available	5
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing	Nov 28, 2022	Federated LearningPersonalized Federated Learning	CodeCode Available	5
FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update	Mar 18, 2024	Federated LearningPersonalized Federated Learning	CodeCode Available	5
An Equivalence Between Data Poisoning and Byzantine Gradient Attacks	Feb 17, 2022	Data PoisoningFederated Learning	CodeCode Available	5
Fusion of Global and Local Knowledge for Personalized Federated Learning	Feb 21, 2023	Federated LearningPersonalized Federated Learning	CodeCode Available	5

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