Split Learning (SL) is a promising Distributed Learning approach in electromyography (EMG) based prosthetic control, due to its applicability within resource-constrained environments. Other learning approaches, such as Deep Learning and Federated Learning (FL), provide suboptimal solutions, since prosthetic devices are extremely limited in terms of processing power and battery life. The viability of implementing SL in such scenarios is caused by its inherent model partitioning, with clients executing the smaller model segment. However, selecting an inadequate cut layer hinders the training process in SL systems. This paper presents an algorithm for optimal cut layer selection in terms of maximizing the convergence rate of the model. The performance evaluation demonstrates that the proposed algorithm substantially accelerates the convergence in an EMG pattern recognition task for improving prosthetic device control.

翻译：分裂学习（SL）是一种在基于肌电图（EMG）的假肢控制中具有前景的分布式学习方法，因其适用于资源受限环境。其他学习方法（如深度学习和联邦学习（FL））提供的方案并非最优，因为假肢设备在处理能力和电池寿命方面极其有限。SL在此类场景中的可行性源于其固有的模型分割特性，即客户端执行较小的模型部分。然而，选择不恰当的切割层会阻碍SL系统的训练过程。本文提出了一种基于最大化模型收敛率的优化分割层选择算法。性能评估表明，所提算法在改善假肢设备控制的EMG模式识别任务中显著加速了收敛过程。