Version 1
: Received: 21 October 2024 / Approved: 21 October 2024 / Online: 22 October 2024 (09:40:58 CEST)
How to cite:
Yang, M.; Huang, D.; Zhan, X. Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development. Preprints2024, 2024101641. https://doi.org/10.20944/preprints202410.1641.v1
Yang, M.; Huang, D.; Zhan, X. Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development. Preprints 2024, 2024101641. https://doi.org/10.20944/preprints202410.1641.v1
Yang, M.; Huang, D.; Zhan, X. Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development. Preprints2024, 2024101641. https://doi.org/10.20944/preprints202410.1641.v1
APA Style
Yang, M., Huang, D., & Zhan, X. (2024). Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development. Preprints. https://doi.org/10.20944/preprints202410.1641.v1
Chicago/Turabian Style
Yang, M., Decheng Huang and Xiaoan Zhan. 2024 "Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development" Preprints. https://doi.org/10.20944/preprints202410.1641.v1
Abstract
This study explores the potential of Federated Learning (FL) to facilitate the sharing and collaboration of medical data in drug development under the premise of privacy protection. While traditional centralized data processing methods limit effective collaboration across agencies due to data privacy and compliance concerns, federated learning avoids the risk of privacy breaches through a distributed architecture that allows participants to train artificial intelligence (AI) models together without sharing raw data. This paper systematically describes the core mechanism of federated learning, including the key technologies such as model parameter updating, differential privacy and homomorphic encryption, and their applications in drug development and medical data processing. Examples, such as NVIDIA Clara's Federated learning application and COVID-19 resource prediction, show that federated learning improves the efficiency of multi-party collaboration and model performance while ensuring data privacy. In addition, this study explores the scalability and generality of federated learning in the medical field, and points out that the technology is not only suitable for drug development, but also has broad cross-industry application potential, especially in areas such as finance and insurance, where data privacy is critical.
Keywords
Federated Learning; Data Privacy; Drug Development; Distributed AI
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
