Summary of Adaptive Split Learning Over Energy-constrained Wireless Edge Networks, by Zuguang Li et al.
Adaptive Split Learning over Energy-Constrained Wireless Edge Networks
by Zuguang Li, Wen Wu, Shaohua Wu, Wei Wang
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
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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 The adaptive split learning (ASL) scheme is designed to dynamically select split points for devices and allocate computing resources for servers in wireless edge networks, aiming to minimize average training latency while considering long-term energy consumption constraints. This approach leverages Lyapunov theory-based online algorithm OPEN, which decomposes the problem into a mixed-integer programming (MIP) problem solvable only with current information. The ASL scheme is shown to reduce average training delay and energy consumption by 53.7% and 22.1%, respectively, compared to existing split learning schemes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The ASL scheme helps devices and servers work together more efficiently to train AI models in a distributed way. This means that devices can learn from each other and the server simultaneously, making training faster and using less energy. The new approach is better than previous methods because it can adapt to changing conditions and make good decisions based on what’s happening now, not just predictions of what might happen later. |
