Semi-Asynchronous Federated Split Learning for Computing-Limited Devices in Wireless Networks

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL enc...

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Veröffentlicht in:	IEEE transactions on wireless communications Jg. 24; H. 6; S. 5196 - 5212
Hauptverfasser:	Ao, Huiqing, Tian, Hui, Ni, Wanli, Nie, Gaofeng, Niyato, Dusit
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Artificial intelligence Computational modeling Convergence convergence analysis Data models Edge computing Energy consumption Federated split learning Heterogeneity Learning Mixed integer Network latency Nonlinear programming Optimization Performance evaluation resource allocation semi-asynchronous model update Servers Training Upper bounds Wireless networks
ISSN:	1536-1276, 1558-2248
Online-Zugang:	Volltext
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