Summary of Energy-efficient Split Learning For Fine-tuning Large Language Models in Edge Networks, by Zuguang Li et al.
Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks
by Zuguang Li, Shaohua Wu, Liang Li, Songge Zhang
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 proposed split learning framework for fine-tuning large language models uses geo-distributed personal data at the network edge. The framework splits LLMs across massive mobile devices and an edge server, addressing device heterogeneity and channel dynamics in edge networks. A CARD algorithm minimizes training delay and energy consumption by optimizing computational resources and communication latency. Simulation results show that the approach reduces average training delay by 70.8% and server’s energy consumption by 53.1%, compared to benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to improve large language models using data from many different devices, like smartphones. This method splits the model into smaller pieces and sends them to these devices to train together with some help from a central “server”. The goal is to make training faster and use less energy while doing it. The approach works well in simulations, reducing the time it takes to train by 70.8% and using 53.1% less energy. |
Keywords
» Artificial intelligence » Fine tuning
