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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 231–240 of 311 papers

TitleStatusHype
Personalized Federated Learning for improving radar based precipitation nowcasting on heterogeneous areas—0
Personalized Federated Learning for Multi-task Fault Diagnosis of Rotating Machinery—0
Personalized Federated Learning for Spatio-Temporal Forecasting: A Dual Semantic Alignment-Based Contrastive Approach—0
Personalized Federated Learning of Probabilistic Models: A PAC-Bayesian Approach—0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving—0
Personalized Federated Learning over non-IID Data for Indoor Localization—0
Personalized Federated Learning Techniques: Empirical Analysis—0
Personalized Federated Learning under Model Dissimilarity Constraints—0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
Personalized Federated Learning via Active Sampling—0
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