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
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Zusammenfassung: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.
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2025.3546448