The increasing complexity of deep neural networks poses significant barriers to democratizing them to resource-limited edge devices. To address this challenge, split federated learning (SFL) has emerged as a promising solution by of floading the primary training workload to a server via model partitioning while enabling parallel training among edge devices. However, although system optimization substantially influences the performance of SFL under resource-constrained systems, the problem remains largely uncharted. In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation. Then, we propose AdaptSFL, a novel resource-adaptive SFL framework, to expedite SFL under resource-constrained edge computing systems. Specifically, AdaptSFL adaptively controls client-side MA and MS to balance communication-computing latency and training convergence. Extensive simulations across various datasets validate that our proposed AdaptSFL framework takes considerably less time to achieve a target accuracy than benchmarks, demonstrating the effectiveness of the proposed strategies.

翻译：深度神经网络的日益复杂化，对其在资源受限边缘设备上的普及构成了重大障碍。为应对这一挑战，分割联邦学习（SFL）通过模型分割将主要训练负担卸载至服务器，同时支持边缘设备并行训练，成为一种有前景的解决方案。然而，尽管系统优化在资源受限环境下对SFL性能影响显著，该问题仍鲜有深入探讨。本文首先对SFL进行收敛性分析，量化模型分割（MS）与客户端模型聚合（MA）对学习性能的影响，为后续研究奠定理论基础。在此基础上，我们提出一种新型资源自适应SFL框架——AdaptSFL，旨在加速资源受限边缘计算系统中的SFL进程。具体而言，AdaptSFL通过自适应控制客户端侧MA与MS，实现通信-计算延迟与训练收敛之间的平衡。跨多数据集的广泛仿真验证表明，与基准方法相比，本文提出的AdaptSFL框架达到目标精度所需时间显著减少，充分证明了所提策略的有效性。