This study analyzes the performance of domain-specific Large Language Models (LLMs) for the medical field by integrating Retrieval-Augmented Generation (RAG) systems within a federated learning framework. Leveraging the inherent advantages of federated learning, such as preserving data privacy and enabling distributed computation, this research explores the integration of RAG systems with models trained under varying client configurations to optimize performance. Experimental results demonstrate that the federated learning-based models integrated with RAG systems consistently outperform their non-integrated counterparts across all evaluation metrics. This study highlights the potential of combining federated learning and RAG systems for developing domain-specific LLMs in the medical field, providing a scalable and privacy-preserving solution for enhancing text generation capabilities.

翻译：本研究通过将检索增强生成（RAG）系统集成到联邦学习框架中，分析了面向医学领域的领域特定大语言模型（LLMs）的性能。利用联邦学习固有的优势（如保护数据隐私和实现分布式计算），本研究探索了在不同客户端配置下训练的模型与RAG系统的集成，以优化性能。实验结果表明，与未集成RAG的模型相比，基于联邦学习并与RAG系统集成的模型在所有评估指标上均表现出更优的性能。本研究强调了结合联邦学习与RAG系统在医学领域开发领域特定大语言模型的潜力，为提升文本生成能力提供了一种可扩展且保护隐私的解决方案。