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TY - JOUR AU - Rahim, Shahnila AU - Kong, Xiao AU - Abdullah, Fatima PY - 2025 DA - 2025/12/09 TI - Navigating Ethical Boundaries in Federated Learning for Biomedical Research JO - Journal of Artificial Intelligence in Bioinformatics T2 - Journal of Artificial Intelligence in Bioinformatics JF - Journal of Artificial Intelligence in Bioinformatics VL - 1 IS - 2 SP - 72 EP - 78 DO - 10.62762/JAIB.2025.703433 UR - https://www.icck.org/article/abs/JAIB.2025.703433 KW - federated learning KW - biomedical ethics KW - data privacy KW - precision medicine KW - AI governance KW - fairness AB - Biomedical research is increasingly shaped by vast and diverse datasets, yet their integration is constrained by privacy concerns, regulatory barriers, and fragmented infrastructures. Federated learning (FL) has emerged as a promising paradigm that enables institutions to collaboratively train machine learning models while keeping sensitive data local. This approach has the potential to accelerate discovery in areas such as precision medicine, rare disease research, and population health by pooling knowledge without centralizing data. However, federated learning also introduces new ethical and governance challenges. Risks of information leakage, inequitable participation, algorithmic bias, unclear accountability, and regulatory complexity must be carefully addressed. This editorial highlights these boundaries and emphasizes that technical solutions alone are insufficient. We argue that responsible deployment of FL requires dedicated ethical frameworks, innovative governance structures, continuous auditing, and inclusive global participation. By embedding responsibility into its design and implementation, federated learning can not only advance biomedical science but also foster trust, equity, and sustainability in the future of data-driven health research. SN - 3068-7535 PB - Institute of Central Computation and Knowledge LA - English ER -
@article{Rahim2025Navigating,
author = {Shahnila Rahim and Xiao Kong and Fatima Abdullah},
title = {Navigating Ethical Boundaries in Federated Learning for Biomedical Research},
journal = {Journal of Artificial Intelligence in Bioinformatics},
year = {2025},
volume = {1},
number = {2},
pages = {72-78},
doi = {10.62762/JAIB.2025.703433},
url = {https://www.icck.org/article/abs/JAIB.2025.703433},
abstract = {Biomedical research is increasingly shaped by vast and diverse datasets, yet their integration is constrained by privacy concerns, regulatory barriers, and fragmented infrastructures. Federated learning (FL) has emerged as a promising paradigm that enables institutions to collaboratively train machine learning models while keeping sensitive data local. This approach has the potential to accelerate discovery in areas such as precision medicine, rare disease research, and population health by pooling knowledge without centralizing data. However, federated learning also introduces new ethical and governance challenges. Risks of information leakage, inequitable participation, algorithmic bias, unclear accountability, and regulatory complexity must be carefully addressed. This editorial highlights these boundaries and emphasizes that technical solutions alone are insufficient. We argue that responsible deployment of FL requires dedicated ethical frameworks, innovative governance structures, continuous auditing, and inclusive global participation. By embedding responsibility into its design and implementation, federated learning can not only advance biomedical science but also foster trust, equity, and sustainability in the future of data-driven health research.},
keywords = {federated learning, biomedical ethics, data privacy, precision medicine, AI governance, fairness},
issn = {3068-7535},
publisher = {Institute of Central Computation and Knowledge}
}
Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 
Journal of Artificial Intelligence in Bioinformatics
ISSN: 3068-7535 (Online)
Email: [email protected]
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