Vol. 4 No. 1 (2024): Hong Kong Journal of AI and Medicine
Articles

Collaborative Federated Learning Techniques for Enhancing Privacy-Preserving Medical Data Exchange

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  • Dr. Dmitry Ivanov
Dr. Dmitry Ivanov
Associate Professor of Computer Science, Belarusian State University, Belarus
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Published 08-06-2024

Keywords

  • Federated Learning,
  • Privacy-Preserving,
  • Medical Data Sharing,
  • Healthcare Institutions,
  • Machine Learning
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Dmitry Ivanov, “Collaborative Federated Learning Techniques for Enhancing Privacy-Preserving Medical Data Exchange”, Hong Kong J. of AI and Med., vol. 4, no. 1, pp. 80–88, Jun. 2024, Accessed: Sep. 09, 2024. [Online]. Available: https://hongkongscipub.com/index.php/hkjaim/article/view/20

Abstract

Federated Learning (FL) has emerged as a promising approach for collaborative machine learning across decentralized entities, enabling privacy-preserving data sharing. In the healthcare sector, the need for sharing medical data while ensuring patient privacy is paramount. This paper explores the application of FL to facilitate the secure exchange of medical data among healthcare institutions. We discuss the challenges and opportunities of FL in this context, highlighting its potential to improve healthcare outcomes while maintaining data privacy. Our research presents a comprehensive review of existing FL frameworks and methodologies applicable to medical data sharing. We also provide insights into the future directions and potential advancements of FL in the healthcare domain.

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