Addressing the challenges of deploying large language models in wireless communication networks, this paper combines low-rank adaptation technology (LoRA) with the splitfed learning framework to propose the federated split learning for large language models (FedsLLM) framework. The method introduced in this paper utilizes LoRA technology to reduce processing loads by dividing the network into client subnetworks and server subnetworks. It leverages a federated server to aggregate and update client models. As the training data are transmitted through a wireless network between clients and both main and federated servers, the training delay is determined by the learning accuracy and the allocation of communication bandwidth. This paper models the minimization of the training delay by integrating computation and communication optimization, simplifying the optimization problem into a convex problem to find the optimal solution. Additionally, it presents a lemma that describes the precise solutions to this problem. Simulation results demonstrate that the proposed optimization algorithm reduces delays by an average of 47.63% compared to unoptimized scenarios.

翻译：针对在无线通信网络中部署大语言模型所面临的挑战，本文结合低秩自适应技术（LoRA）与分割联邦学习框架，提出了面向大语言模型的联邦分割学习（FedsLLM）框架。该方法利用LoRA技术将网络划分为客户端子网络与服务器子网络以降低处理负载，并借助联邦服务器对客户端模型进行聚合与更新。由于训练数据需通过无线网络在客户端与主服务器、联邦服务器之间传输，训练延迟由学习精度与通信带宽分配共同决定。本文通过融合计算与通信优化对训练延迟最小化进行建模，将优化问题简化为凸问题以寻求最优解，并提出了描述该问题精确解的引理。仿真结果表明，所提出的优化算法相较于未优化场景平均可降低47.63%的延迟。