Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices
This work addresses the challenge of efficient training for prosthetic devices in resource-constrained environments, representing an incremental improvement over existing methods.
The paper tackles the problem of slow convergence in Split Learning for EMG prosthetic control by introducing an algorithm for optimal cut layer selection, which substantially accelerates convergence in EMG pattern recognition tasks.
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.