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.

我们设计了一种自适应分裂学习（ASL）方案，该方案可以在无线边缘网络中为设备动态选择分裂点并为服务器分配计算资源，以减少训练延迟和能量消耗。采用在线算法 OPEN 和两层优化方法解决优化问题，该方案相比现有的分裂学习方案，平均减少了 53.7% 的训练延迟和 22.1% 的能量消耗。