CroSSHeteroFL: Cross-Stratified Sampling Composition-Fitting to Federated Learning for Heterogeneous Clients

In the large-scale deployment of federated learning (FL) systems, the heterogeneity of clients, such as mobile phones and Internet of Things (IoT) devices with different configurations, constitutes a significant problem regarding fairness, training performance, and accuracy. Such system heterogeneit...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.148011-148025
Hauptverfasser:	Tinh, Vo Phuc, Son, Hoang Hai, Nam, Nguyen Hoang, Dang, Duc Ngoc Minh, Le, Duy-Dong, Nguyen, Thai-Binh, Pham, Thanh-Qui, Nguyen, van-Luong, Huynh, Duy-Thanh, Khoa, Tran Anh
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models capability computation complexity Computational complexity Computational modeling Computer architecture Data models Federated learning heterogeneity Internet of Things Noise measurement resource-adaptive Servers Training
Online-Zugang:	Volltext
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