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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 291–300 of 311 papers

TitleStatusHype
Personalized Federated Learning for Heterogeneous Clients with Clustered Knowledge Transfer—0
Private Multi-Task Learning: Formulation and Applications to Federated LearningCode0
Federated Asymptotics: a model to compare federated learning algorithms—0
Personalized Federated Learning with Clustering: Non-IID Heart Rate Variability Data Application—0
New Metrics to Evaluate the Performance and Fairness of Personalized Federated Learning—0
Sparse Personalized Federated LearningCode0
Personalized Federated Learning over non-IID Data for Indoor Localization—0
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach—0
Personalized Federated Learning with Contextualized Generalization—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
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