Summary of Federated Learning and Rag Integration: a Scalable Approach For Medical Large Language Models, by Jincheol Jung et al.
Federated Learning and RAG Integration: A Scalable Approach for Medical Large Language Models
by Jincheol Jung, Hongju Jeong, Eui-Nam Huh
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study investigates the performance of Large Language Models (LLMs) tailored to the medical field by integrating Retrieval-Augmented Generation (RAG) systems within a federated learning framework. By leveraging federated learning’s advantages, such as data privacy preservation and distributed computation, this research explores the integration of RAG systems with models trained under varying client configurations to optimize performance. The results show that the federated learning-based models integrated with RAG systems consistently outperform their non-integrated counterparts across all evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how Large Language Models can be used in medicine by combining two techniques: Retrieval-Augmented Generation and federated learning. Federated learning is a way to train models without sharing data, which is important for privacy reasons. The study shows that when you combine this with RAG, the models perform better. This could lead to more accurate text generation in the medical field. |
Keywords
» Artificial intelligence » Federated learning » Rag » Retrieval augmented generation » Text generation
