Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices
CoRR(2024)
摘要
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.
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