Split learning (SL) is a promising approach for training artificial intelligence (AI) models, in which devices collaborate with a server to train an AI model in a distributed manner, based on a same fixed split point. However, due to the device heterogeneity and variation of channel conditions, this way is not optimal in training delay and energy consumption. In this paper, we design an adaptive split learning (ASL) scheme which can dynamically select split points for devices and allocate computing resource for the server in wireless edge networks. We formulate an optimization problem to minimize the average training latency subject to long-term energy consumption constraint. The difficulties in solving this problem are the lack of future information and mixed integer programming (MIP). To solve it, we propose an online algorithm leveraging the Lyapunov theory, named OPEN, which decomposes it into a new MIP problem only with the current information. Then, a two-layer optimization method is proposed to solve the MIP problem. Extensive simulation results demonstrate that the ASL scheme can reduce the average training delay and energy consumption by 53.7% and 22.1%, respectively, as compared to the existing SL schemes.

翻译：分割学习（SL）是一种有前景的人工智能（AI）模型训练方法，其中设备与服务器协作，基于相同的固定分割点以分布式方式训练AI模型。然而，由于设备异构性和信道条件变化，这种方法在训练延迟和能量消耗方面并非最优。本文设计了一种自适应分割学习（ASL）方案，该方案能够动态选择设备的分割点，并为无线边缘网络中的服务器分配计算资源。我们构建了一个优化问题，旨在最小化长期能量消耗约束下的平均训练延迟。解决该问题的难点在于缺乏未来信息以及混合整数规划（MIP）。为此，我们提出了一种基于李雅普诺夫理论的在线算法，命名为OPEN，该算法将原问题分解为仅需当前信息的新MIP问题。随后，我们提出了一种双层优化方法来求解该MIP问题。大量仿真结果表明，与现有SL方案相比，ASL方案可将平均训练延迟和能量消耗分别降低53.7%和22.1%。