In the rapidly evolving landscape of large language models (LLMs) and mobile edge computing, the need for efficient service delivery to mobile users with constrained computational resources has become paramount. Addressing this, our paper delves into a collaborative framework for model training where user data and model adapters are shared with servers to optimize performance. Within this framework, users initially update the first several layers of the adapters while freezing the other layers of them, leveraging their local datasets. Once this step is complete, these partially trained parameters are transmitted to servers. The servers, equipped with more robust computational capabilities, then update the subsequent layers. After this training, they send the enhanced parameters back to the users. This collaborative training approach ensures that mobile users with limited computational capacities can still benefit from advanced LLM services without being burdened by exhaustive computations. Central to our methodology is the DASHF algorithm, which encapsulates the Dinkelbach algorithm, alternating optimization, semidefinite relaxation (SDR), the Hungarian method, and a pioneering fractional programming technique from our recent IEEE JSAC paper "Human-Centric Resource Allocation in the Metaverse over Wireless Communications". The crux of DASHF is its capability to reformulate an optimization problem as Quadratically Constrained Quadratic Programming (QCQP) via meticulously crafted transformations, making it solvable by SDR and the Hungarian algorithm. Through extensive simulations, we demonstrate the effectiveness of the DASHF algorithm, offering significant insights for the advancement of collaborative LLM service deployments.

翻译：在大语言模型和移动边缘计算快速发展的背景下，面向计算资源受限的移动用户提供高效服务已成为关键需求。针对这一问题，本文深入探讨了一种协作训练框架，该框架通过将用户数据和模型适配器与服务器共享，以实现性能优化。在此框架中，用户首先利用本地数据集对适配器前几层进行更新，同时冻结其余层。完成该步骤后，这些部分训练的模型参数被传输至服务器。服务器凭借更强的计算能力继续更新后续层，训练完成后将优化后的参数回传至用户。这种协作训练方法使得计算能力有限的移动用户无需承担繁重计算任务，即可受益于先进的大语言模型服务。我们方法的核心是DASHF算法，该算法整合了Dinkelbach算法、交替优化、半定松弛、匈牙利方法，以及我们近期发表在IEEE JSAC期刊《面向元宇宙的人本资源分配无线通信》中提出的开创性分数规划技术。DASHF算法的关键在于通过精心设计的变换将优化问题重构为二次约束二次规划形式，从而可通过半定松弛和匈牙利算法求解。大量仿真实验证明了DASHF算法的有效性，为协同大语言模型服务部署的发展提供了重要启示。