Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices
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Abstract (arXiv)
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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References · 7
- 2018Distributed learning of deep neural network over multiple agentscited by 277
- 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
- 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 76
- 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 74
- 2019Split Learning for collaborative deep learning in healthcarecited by 72
- 2020Advancements of federated learning towards privacy preservation: from federated learning to split learningcited by 34
- 2022A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networkscited by 22
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