Work

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

Year: 2024

Type: article

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. 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, especially in areas such as finance and insurance, where data privacy is critical.

Source: Applied and Computational Engineering

DOI: 10.54254/2755-2721/2025.ld17879

Authors: Mingxuan Yang, Decheng Huang, Weixiang Wan, Meizhizi Jin

Institutions: Brown University, University of Pennsylvania, University of Electronic Science and Technology of China, New York University

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4405122419

FWCI: 1.672

Cites:

Cited by: 11

Related to: 10