Federated Large Language Models for A Trustworthy and Privacy-Preserving Healthcare: Applications, Challenges, and...

Area of Science:

• Digital Health
• Artificial Intelligence
• Medical Informatics

Background:

• Federated Large Language Models (FLLMs) promise to revolutionize digital health by enabling advanced natural language processing on sensitive patient data while maintaining privacy.
• Key challenges include the significant computational and communication demands of large models on resource-limited Internet-of-Medical-Things (IoMT) devices and performance issues due to Non-Independent and Identically Distributed (Non-IID) patient data.

Purpose of the Study:

• To provide a comprehensive survey of Federated Learning (FL) applied to Large Language Models (LLMs) within the healthcare domain (FedMed-LLMs).
• To review the foundational concepts of FL and medical LLMs, discuss current applications, and analyze associated challenges.
• To identify core research problems and propose a future research agenda for developing robust and scalable FedMed-LLMs systems.

Main Methods:

• Literature review of existing research on Federated Learning, Large Language Models in healthcare, and their integration.
• Analysis of current FedMed-LLMs applications, focusing on privacy, robustness, and trustworthiness concerns.
• Identification and categorization of key research challenges and future research directions.

Main Results:

• The survey covers the fundamentals of FL and medical LLMs, highlighting their potential in healthcare applications.
• Identified critical issues hindering adoption, including computational/communication burdens and Non-IID data challenges.
• A detailed examination of privacy, robustness, and trustworthiness concerns associated with FedMed-LLMs is presented.

Conclusions:

• Federated Learning for LLMs in Healthcare (FedMed-LLMs) presents a promising avenue for secure and advanced digital health solutions.
• Addressing the identified challenges in computational efficiency, data heterogeneity, privacy, robustness, and trustworthiness is crucial for successful implementation.
• The proposed research agenda offers a roadmap for future advancements in building scalable and reliable FedMed-LLMs.