Federated Learning for Healthcare: Balancing Data Privacy and Model Accuracy
DOI:
https://doi.org/10.47672/ajce.2634Keywords:
Federated Learning, Machine Learning, HealthcareAbstract
Purpose: Federated Learning (FL) is transforming the way machine learning models are trained by allowing institutions to collaborate without sharing sensitive data. This is especially valuable in healthcare, where patient records are often stored separately across hospitals and research centers. This decentralized approach allows healthcare providers, researchers, and organizations to leverage collective intelligence from distributed datasets, leading to advancements in diagnostics, treatment personalization, and patient outcomes.
Materials and Methods: However, the adoption of FL in healthcare is not without challenges, particularly in balancing the dual objectives of preserving data privacy and maintaining model accuracy. In this article, we explore how FL is being applied in healthcare, examining the balance between protecting patient privacy and ensuring high model accuracy. We review recent advancements in FL, focusing on privacy-preserving techniques such as differential privacy, secure multi-party computation, and homomorphic encryption, and their impact on model performance.
Findings: Through a comprehensive analysis of case studies and empirical research, we highlight the potential of FL to revolutionize healthcare applications, including medical imaging, electronic health records (EHR) analysis, and genomic research. We discuss recent advancements, key challenges, and innovative solutions, drawing insights from various studies.
Implications to Theory, Practice and Policy: Finally, we highlight future directions and provide practical recommendations for researchers and professionals looking to implement FL in medical settings.
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References
Anand, A., 2023. GDPR and Healthcare: Balancing Data Privacy and Access to Medical Information. NUJS J. Regul. Stud., 8, p.27.
Bhosale, K.S., Nenova, M. and Iliev, G., 2021, September. A study of cyber attacks: In the healthcare sector. In 2021 Sixth Junior Conference on Lighting (Lighting) (pp. 1-6). IEEE.
Kalapaaking, A.P., Stephanie, V., Khalil, I., Atiquzzaman, M., Yi, X. and Almashor, M., 2022. Smpc-based federated learning for 6g-enabled internet of medical things. IEEE Network, 36(4), pp.182-189.
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A. and Qadir, J., 2023. Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Computers in Biology and Medicine, 158, p.106848.
Li, Tian, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. "Federated learning: Challenges, methods, and future directions." IEEE signal processing magazine 37, no. 3 (2020): 50-60.
Liu, H., Li, C., Liu, B., Wang, P., Ge, S. and Wang, W., 2021, December. Differentially private learning with grouped gradient clipping. In Proceedings of the 3rd ACM International Conference on Multimedia in Asia (pp. 1-7).
Mbonihankuye, S., Nkunzimana, A. and Ndagijimana, A. 2019 ‘Healthcare Data Security Technology: HIPAA compliance’, Wireless Communications and Mobile Computing, 2019, pp. 1–7. doi:10.1155/2019/1927495.
Mehrjou, A., Soleymani, A., Buchholz, A., Hetzel, J., Schwab, P. and Bauer, S., 2022. Federated learning in multi-center critical care research: A systematic case study using the eicu database. arXiv preprint arXiv:2204.09328.
Qayyum, A., Ahmad, K., Ahsan, M.A., Al-Fuqaha, A. and Qadir, J., 2022. Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge. IEEE Open Journal of the Computer Society, 3, pp.172-184.
Reuters. (2015). Anthem Hacking Points to Security Challenges of Healthcare Data. Retrieved from https://www.reuters.com/article/us-anthem-cybersecurity
Seh AH, Zarour M, Alenezi M, Sarkar AK, Agrawal A, Kumar R, Khan RA. Healthcare Data Breaches: Insights and Implications. Healthcare (Basel). 2020 May 13;8(2):133. doi: 10.3390/healthcare8020133..
Sheller, M.J., Edwards, B., Reina, G.A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R.R. and Bakas, S., 2020. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Scientific reports, 10(1), p.12598.
Staynings, Richard. "Cybersecurity." In Digital Health Entrepreneurship, pp. 131-155. Cham: Springer International Publishing, 2023.
Subramanian, M., Rajasekar, V., VE, S., Shanmugavadivel, K. and Nandhini, P.S., 2022. Effectiveness of decentralized federated learning algorithms in healthcare: a case study on cancer classification. Electronics, 11(24), p.4117.
Sun, T., Li, D. and Wang, B., 2022. Decentralized federated averaging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4), pp.4289-4301.
Tan, Y., Long, G., Ma, J., Liu, L., Zhou, T. and Jiang, J., 2022. Federated learning from pre-trained models: A contrastive learning approach. Advances in neural information processing systems, 35, pp.19332-19344.
Truex, S., Baracaldo, N., Anwar, A., Steinke, T., Ludwig, H., Zhang, R. and Zhou, Y., 2019, November. A hybrid approach to privacy-preserving federated learning. In Proceedings of the 12th ACM workshop on artificial intelligence and security (pp. 1-11).
Wei, Kang, et al. "Federated learning with differential privacy: Algorithms and performance analysis." IEEE transactions on information forensics and security 15 (2020): 3454-3469.
Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2023). A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513-535.
Wu, C., Wu, F., Lyu, L., Huang, Y. and Xie, X., 2022. Communication-efficient federated learning via knowledge distillation. Nature communications, 13(1), p.2032.
Yang, X., Huang, W., & Ye, M. (2023). Dynamic personalized federated learning with adaptive differential privacy. Advances in Neural Information Processing Systems, 36, 72181-72192.
Yang, Z., Zhou, M., Yu, H., Sinnott, R.O. and Liu, H., 2022. Efficient and secure federated learning with verifiable weighted average aggregation. IEEE Transactions on Network Science and Engineering, 10(1), pp.205-222.
Zhang, Jinghui, Dingyang Lv, Qiangsheng Dai, Fa Xin, and Fang Dong. "Noise-aware local model training mechanism for federated learning." ACM Transactions on Intelligent Systems and Technology 14, no. 4 (2023): 1-22.
Zhu, Hangyu, Jinjin Xu, Shiqing Liu, and Yaochu Jin. "Federated learning on non-IID data: A survey." Neurocomputing 465 (2021): 371-390.
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Copyright (c) 2025 Nishchai Jayanna Manjula, Kiran Randhi, Srinivas Reddy Bandarapu
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