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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Vydané v:	IEEE transactions on wireless communications Ročník 24; číslo 6; s. 5196 - 5212
Hlavní autori:	Ao, Huiqing, Tian, Hui, Ni, Wanli, Nie, Gaofeng, Niyato, Dusit
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.06.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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 encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness.
AbstractList	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 encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness.
Author	Ni, Wanli Nie, Gaofeng Tian, Hui Ao, Huiqing Niyato, Dusit
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SubjectTerms	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
Title	Semi-Asynchronous Federated Split Learning for Computing-Limited Devices in Wireless Networks
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