As mobile devices increasingly become focal points for advanced applications, edge computing presents a viable solution to their inherent computational limitations, particularly in deploying large language models (LLMs). However, despite the advancements in edge computing, significant challenges remain in efficient training and deploying LLMs due to the computational demands and data privacy concerns associated with these models. This paper explores a collaborative training framework that integrates mobile users with edge servers to optimize resource allocation, thereby enhancing both performance and efficiency. Our approach leverages parameter-efficient fine-tuning (PEFT) methods, allowing mobile users to adjust the initial layers of the LLM while edge servers handle the more demanding latter layers. Specifically, we formulate a multi-objective optimization problem to minimize the total energy consumption and delay during training. We also address the common issue of instability in model performance by incorporating stability enhancements into our objective function. Through novel fractional programming technique, we achieve a stationary point for the formulated problem. Simulations demonstrate that our method reduces the energy consumption as well as the latency, and increases the reliability of LLMs across various mobile settings.

本研究解决了在移动边缘计算中高效训练大型语言模型（LLMs）所面临的资源分配与数据隐私问题。我们提出了一种协作训练框架，结合移动用户与边缘服务器的资源，以提高性能和效率，通过多目标优化方法降低能耗和延迟，同时增强模型的稳定性。模拟结果表明，该方法在多种移动环境中有效减少能耗和延迟，提高了LLM的可靠性。

移动边缘计算中稳定的LLM训练资源分配