Leveraging Federated Learning for Secure Transfer and Deployment of ML Models in Healthcare
Zlate Dodevski, Tanja Pavleska, Vladimir Trajkovik · 2024
Federated learning (FL) represents a pivotal advancement in applying Machine Learning (ML) in healthcare. It addresses the challenges of data privacy and security by facilitating model transferability across institutions. This paper explores the effective employment of FL to enhance the deployment of large language models (LLMs) in healthcare settings while maintaining stringent privacy standards. Through a detailed examination of the challenges in applying LLMs to the healthcare domain, including privacy, security, regulatory constraints, and training data quality, we present a federated learning architecture tailored for LLMs in healthcare. This architecture outlines the roles and responsibilities of participating entities, providing a framework for secure collaboration. We further analyze privacy-preserving techniques such as differential privacy and secure aggregation in the context of federated LLMs for healthcare, offering insights into their practical implementation. Our findings suggest that federated learning can significantly enhance the capabilities of LLMs in healthcare while preserving patient privacy. In addition, we also identify persistent challenges in areas such as computational and communicational efficiency, lack of benchmarks and tailored FL aggregation algorithms applied to LLMs, model performance, and ethical concerns in participant selection. By critically evaluating the proposed approach and highlighting its potential benefits and limitations in real-world healthcare settings, this work provides a foundation for future research in secure and privacy-preserving ML deployment in healthcare.