Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development

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. 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.