Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness

Research into Robust Federated Learning Methods Driven by Heterogeneity Awareness

Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—com...

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Bibliographic Details
Main Authors:	Junhui Song, Zhangqi Zheng, Afei Li, Zhixin Xia, Yongshan Liu
Format:	Article
Language:	English
Published:	MDPI AG 2025-07-01
Series:	Applied Sciences
Subjects:	federated learning data heterogeneity heterogeneity-aware weighted aggregation multi-loss function
Online Access:	https://www.mdpi.com/2076-3417/15/14/7843
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