Summary of Convergence Rate Maximization For Split Learning-based Control Of Emg Prosthetic Devices, by Matea Marinova et al.
Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices
by Matea Marinova, Daniel Denkovski, Hristijan Gjoreski, Zoran Hadzi-Velkov, Valentin Rakovic
First submitted to arxiv on: 6 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a Distributed Learning approach called Split Learning (SL) for electromyography (EMG) based prosthetic control, particularly suitable for resource-constrained environments. Unlike Deep Learning and Federated Learning, SL is more feasible due to its inherent model partitioning, allowing clients to execute smaller model segments. However, the training process can be hindered by inadequate cut layer selection. This study presents an algorithm for optimal cut layer selection to maximize the convergence rate of the model in SL systems. The evaluation demonstrates that this algorithm accelerates the convergence rate in EMG pattern recognition tasks for improving prosthetic device control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Split Learning is a new way to make prosthetics work better using tiny bits of information from different parts of the body. It’s like a team effort, where each part sends its own piece of the puzzle and the computer puts it all together. But sometimes the computer gets stuck because it doesn’t know how to put the pieces together correctly. This study figures out a way for the computer to choose the right pieces so it can work faster and better. The results show that this new approach helps prosthetics control devices more effectively. |
Keywords
* Artificial intelligence * Deep learning * Federated learning * Pattern recognition
