Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks
This work addresses energy and delay challenges for deploying LLMs on resource-constrained edge devices, though it is incremental as it builds on existing split learning methods.
The paper tackles the problem of fine-tuning large language models on edge networks by proposing an energy-efficient split learning framework, which reduces average training delay by 70.8% and server energy consumption by 53.1% compared to benchmarks.
In this letter, we propose an energy-efficient split learning (SL) framework for fine-tuning large language models (LLMs) using geo-distributed personal data at the network edge, where LLMs are split and alternately across massive mobile devices and an edge server. Considering the device heterogeneity and channel dynamics in edge networks, a \underline{C}ut l\underline{A}yer and computing \underline{R}esource \underline{D}ecision (CARD) algorithm is developed to minimize training delay and energy consumption. Simulation results demonstrate that the proposed approach reduces the average training delay and server's energy consumption by 70.8% and 53.1%, compared to the benchmarks, respectively.