Fine-tuned large language models （LLMs） perform exceptionally well in various tasks， but centralized training poses user privacy leakage risks. Federated learning （FL） mitigates data sharing issues through local training， yet the large parameter size of LLMs challenges resource-constrained devices and communication bandwidth， making deployment in edge networks difficult. Considering split learning （SL）， federated split learning can effectively address these issues. Given the more pronounced influence of deep-layer model weights and the discovery that training certain layers yields slightly lower accuracy compared to training the entire model， we opt to split the model based on Transformer layers. Additionally， utilizing low-rank adaption （LoRA） can further reduce resource overhead and enhance security. Therefore， at each device， we only perform LoRA and training on the final few layers. These adapted layers are then uploaded to the server for aggregation. From the perspective of cost reduction and ensuring model performance， we propose a fine-tuning method for the pre-trained model RoBERTa based on federated split learning and LoRA. By jointly optimizing the computational frequency of edge devices and the rank of model fine-tuning， we maximize the rank to improve model accuracy under resource constraints. Simulation results indicate that only training the last three layers of the LLMs can improve model accuracy within a certain range （1—32） by increasing the rank. Additionally， increasing the per-round delay and the energy threshold of devices can further enhance model accuracy.