DF-RAG: A Dual Federated Retrieval-Augmented Generation Framework for Collaborative Medical AI

Abstract

This paper introduces Dual Federated Retrieval-Augmented Generation (DF-RAG), a framework addressing privacy, interpretability, and reliability challenges in AI-driven healthcare. Large Language Models (LLMs) typically depend on centralized training and static data, causing inaccuracies ("hallucinations") and privacy risks. DF-RAG mitigates these issues through Federated Fine-Tuning (FFT) and Federated Knowledge Graphs (FKGs). FFT enables secure collaborative model refinement across institutions using encrypted updates, ensuring compliance with HIPAA and GDPR. Concurrently, FKGs offer real-time, validated medical knowledge retrieval, reducing inaccuracies and enhancing interpretability. Initial evaluations suggest DF-RAG improves diagnostic accuracy, scalability, and patient privacy, promising significant advances in clinical decision-making and personalized medicine.

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