Harish Bishnoi
Independent Researcher
India
Abstract
The rapid digitization of healthcare has led to an exponential increase in sensitive medical data, necessitating robust privacy-preserving techniques for data analytics. Federated Learning (FL), an emerging distributed machine learning paradigm, enables collaborative model training across multiple healthcare institutions without exposing raw patient data, thereby preserving privacy. This survey paper reviews the state-of-the-art applications of federated learning in healthcare data privacy up to 2021. It synthesizes methodologies, challenges, and solutions proposed in the literature, focusing on privacy protection, data heterogeneity, communication efficiency, and regulatory compliance. We perform a statistical analysis of key publications and identify research gaps. The survey aims to provide an engineering perspective on leveraging FL for privacy-preserving healthcare analytics and outlines future research directions.
Keywords: Federated Learning, Healthcare, Data Privacy, Distributed Machine Learning, Privacy-Preserving Analytics
REFERENCES
- Rieke, N., Hancox, J., Li, W., Milletari, F., Roth, H. R., Albarqouni, S., … & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3(1), 1-7. (Though 2020, relevant foundational paper.)
- Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10(1), 1-12.
- Kaissis, G., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2(6), 305-311.
- Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50-60.
- Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., … & Ramage, D. (2019). Towards federated learning at scale: System design. Proceedings of the 2nd SysML Conference.
- Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021). Federated learning for healthcare informatics. Journal of Healthcare Informatics Research, 5(1), 1-19.
- Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 10(2), 1-19.
- Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, 1310-1321.
- McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS).
- Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., … & Zhao, S. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977.
- Li, X., Huang, K., Yang, W., Wang, S., & Zhang, Z. (2020). On the convergence of FedAvg on non-IID data. arXiv preprint arXiv:1907.02189.
- Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308-318.
