Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models

TL;DR

This paper presents a scalable, privacy-preserving method that combines federated learning with Retrieval-Augmented Generation to improve medical domain-specific large language models, demonstrating superior performance over traditional approaches.

Contribution

It introduces an integrated federated learning and RAG framework specifically designed for medical LLMs, enhancing performance while maintaining data privacy.

Findings

Federated RAG models outperform non-integrated models across metrics.

The approach preserves data privacy in medical LLM training.

Scalable solution for domain-specific medical text generation.

Abstract

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.