Federated Learning in Healthcare: An Analytical Study on Privacy-Preserving Data Sharing and Clinical Decision Support
DOI:
https://doi.org/10.32628/IJSRST25126233Keywords:
Healthcare, Artificial Intelligence in Healthcare, Decentralized Machine Learning, Secure Aggregation, Clinical Decision Support Systems, Federated Learning, Privacy-Preserving Data Sharing, Collaborative Learning, Predictive AnalyticsAbstract
Learning Federated, which permits cooperative model training across dispersed clinical datasets without jeopardizing patient privacy, has become a paradigm shift in healthcare. An analytical examination of FL for data sharing that protects privacy and its possible application in clinical decision support systems is presented in this work. The study investigates how data silos, legal restrictions, and security threats related to traditional centralized methods are addressed via decentralized learning frameworks. The effectiveness of FL algorithms, encryption methods, and secure aggregation strategies in safeguarding private medical data is emphasized. The study also emphasizes how FL-driven models can enhance predictive analytics, therapy customization, and diagnostic accuracy in healthcare applications. Comparative studies and experimental findings show that FL improves the scalability and dependability of clinical decision-making while maintaining anonymity. The results highlight federated learning's potential as a long-term means of promoting data-driven healthcare breakthroughs while maintaining moral and legal observance.
Downloads
References
Hitaj, B., Ateniese, G., & Pérez-Cruz, F. (2019). Deep models under the GAN: Information leakage from collaborative deep learning. Proceedings of the ACM Conference on Computer and Communications Security.
Kaissis, G., Makowski, M., Rückert, D., & Braren, R. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305–311. DOI: https://doi.org/10.1038/s42256-020-0186-1
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2020). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 4, 1–19. DOI: https://doi.org/10.1007/s41666-020-00082-4
Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., … & Bakas, S. (2021). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 11, 12598. DOI: https://doi.org/10.1038/s41598-020-69250-1
Kairouz, P., McMahan, B., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210. DOI: https://doi.org/10.1561/9781680837896
Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., … & Cardoso, M. J. (2022). The future of digital health with federated learning. npj Digital Medicine, 5, 1–7.
Li, X., Gu, Y., Dvornek, N. C., Staib, L. H., Ventola, P., & Duncan, J. S. (2023). Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Medical Image Analysis, 81, 102530.
Zhang, Y., Chen, M., & Sun, X. (2024). Federated transfer and reinforcement learning in personalized healthcare. IEEE Transactions on Neural Networks and Learning Systems.
Wang, H., Zhao, T., & Lin, Y. (2025). Trustworthy federated learning frameworks for healthcare: Privacy, fairness, and interpretability. Journal of Biomedical Informatics, 150, 104678.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0
