将检索增强生成融入联邦学习，提升医疗大模型性能与隐私保护。
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models
- 在联邦学习框架中引入RAG系统，实现分布式训练与知识检索融合。
- 集成RAG的模型在所有评估指标上均优于传统方法，表现更稳定。
- 适合医疗领域需隐私保护与高性能生成的场景，可扩展性强。
本研究通过在联邦学习框架中整合检索增强生成（RAG）系统，分析特定领域大语言模型（LLM）在医疗领域的性能。利用联邦学习在数据隐私保护和分布式计算方面的优势，探索不同客户端配置下RAG系统与模型的结合效果。实验结果表明，集成RAG的联邦学习模型在所有评估指标上均持续优于未集成的对照模型，展现出更强的文本生成能力。该研究验证了联邦学习与RAG结合在医疗领域构建专用大模型中的潜力，提供了一种可扩展且隐私友好的解决方案。
原文摘要 · Abstract (English)
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.
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