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

Mingxuan Yang; Decheng Huang; Weixiang Wan; Meizhizi Jin

doi:10.54254/2755-2721/2025.LD17879

Research Article

Open access

Published on 6 December 2024

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Yang,M.;Huang,D.;Wan,W.;Jin,M. (2024). Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development. Applied and Computational Engineering,108,7-13.

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

Mingxuan Yang *^,1, Decheng Huang ², Weixiang Wan ³, Meizhizi Jin ⁴

¹ Innovation Management and Entrepreneurship, Brown University, RI, USA
² Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, PA, USA
³ Electronics & Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
⁴ Management Information Systems, New York University, NY, USA

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2755-2721/2025.LD17879

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.

Keywords

Federated Learning, Data Privacy, Drug Development, Distributed AI

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Cite this article

Yang,M.;Huang,D.;Wan,W.;Jin,M. (2024). Federated Learning for Privacy-Preserving Medical Data Sharing in Drug Development. Applied and Computational Engineering,108,7-13.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning

Conference website: https://2025.confspml.org/

ISBN：978-1-83558-711-9(Print) / 978-1-83558-712-6(Online)

Conference date: 12 January 2025

Editor：Stavros Shiaeles, Bilyaminu Romo Auwal

Series: Applied and Computational Engineering

Volume number: Vol.108

ISSN：2755-2721(Print) / 2755-273X(Online)

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