Federated Learning with Differential Privacy Guarantees for Heterogeneous Distributed Medical Imaging Datasets

Lenin P

Authors

Lenin P Federated Learning Systems Engineer, India. Author

Keywords:

Federated Learning, Differential Privacy, Medical Imaging, Healthcare Artificial Intelligence, Data Heterogeneity, Privacy-Preserving Machine Learning, Distributed Learning; Deep Learning, Medical Image Analysis, Privacy–Utility Trade-off

Abstract

Purpose: This study develops a privacy-preserving federated learning framework for heterogeneous medical imaging datasets distributed across multiple healthcare institutions. The research addresses the challenge of collaboratively training clinically useful imaging models without centralizing sensitive patient images, while providing measurable differential privacy protection and accommodating institutional differences in scanners, patient populations, imaging protocols, disease prevalence, and data volume.

Design/methodology/approach: The study adopts a computational research design integrating federated optimization, client-level data governance, differential privacy, secure model-update exchange, and heterogeneity-aware aggregation. Medical images remain within participating institutions. Local models are trained independently before protected parameter updates are communicated to an aggregation server. Gradient clipping and calibrated Gaussian noise provide differential privacy protection, while privacy accounting tracks cumulative exposure. Performance is assessed through discrimination, sensitivity, specificity, calibration, privacy expenditure, communication requirements, and cross-site consistency.

Findings: The proposed framework indicates that privacy protection, institutional heterogeneity, and model utility should be treated as interconnected design requirements rather than independent technical problems. Heterogeneity-aware federated optimization can reduce instability caused by non-identically distributed institutional data, while differential privacy limits information leakage from communicated updates. Stronger privacy protection can reduce predictive utility, making privacy-budget selection and local training configuration important components of deployment.

Practical implications: Hospitals and medical research networks can use the framework to collaborate on imaging models without routinely transferring raw patient images to a central repository. The approach supports privacy-conscious multicenter research, institutional data control, auditable privacy accounting, and broader participation by organizations that cannot freely exchange sensitive medical images.

Originality/value: The study integrates medical-imaging heterogeneity, federated optimization, differential privacy guarantees, cross-site validation, and clinically oriented evaluation within a unified research architecture. It emphasizes transparent privacy–utility analysis and institutional performance consistency rather than relying solely on aggregate model accuracy.

References

1. Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, 308–318.

2. Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017). Practical secure aggregation for privacy-preserving machine learning. Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, 1175–1191.

3. Chakravarti S, Fenech R, Khan KMA, Prasanth VS (2026) Designing industry-driven vocational education: a competency-based case study from the United Arab Emirates. International Journal of Special Education 41(2):434–443. https://internationalsped.com/index.php/ijse/article/view/4590

4. Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C. S., Wang, C. H., Hsu, C. N., Lee, C. K., Ruan, P., Xu, D., Wu, D., Huang, E., Kitamura, F. C., Lacey, G., et al. (2021). Federated learning for predicting clinical outcomes in patients with COVID-19. Nature Medicine, 27, 1735–1743.

5. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Theory of Cryptography Conference, 265–284.

6. Dwork, C., & Roth, A. (2014). The Algorithmic Foundations of Differential Privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.

7. Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. NIPS Workshop on Machine Learning on the Phone and Other Consumer Devices.

8. Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020). Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2, 305–311.

9. Chakravarti S, Khan K, Daniel L, Chakraborty M (2026) Women’s learning in cultural borderlands: a cross-regional study of culture, technology, and agency. Journal of Intelligent Decision Making and Information Science 3(13s):302–318.

10. Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M. M., Saleh, A., Makowski, M., Rueckert, D., & Braren, R. (2021). End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nature Machine Intelligence, 3, 473–484.

11. Kairouz, P., McMahan, H. B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A. N., Bonawitz, K., Charles, Z., Cormode, G., Cummings, R., et al. (2021). Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 14(1–2), 1–210.

12. Karimireddy, S. P., Kale, S., Mohri, M., Reddi, S., Stich, S., & Suresh, A. T. (2020). SCAFFOLD: Stochastic controlled averaging for federated learning. Proceedings of the International Conference on Machine Learning, 5132–5143.

13. 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.

14. Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2020). Federated optimization in heterogeneous networks. Proceedings of Machine Learning and Systems, 2, 429–450.

15. Li, W., Milletarì, F., Xu, D., Rieke, N., Hancox, J., Zhu, W., Baust, M., Cheng, Y., Ourselin, S., Cardoso, M. J., & Feng, A. (2019). Privacy-preserving federated brain tumour segmentation. Machine Learning in Medical Imaging, 133–141.

16. McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Agüera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the International Conference on Artificial Intelligence and Statistics, 1273–1282.

17. Mironov, I. (2017). Rényi differential privacy. IEEE Computer Security Foundations Symposium, 263–275.

18. Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. npj Digital Medicine, 3, 119.

19. Sheller, M. J., Reina, G. A., Edwards, B., Martin, J., & Bakas, S. (2019). Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 92–104.

20. Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., Milchenko, M., Xu, W., Marcus, D., Colen, R. R., & Bakas, S. (2020). Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data. Scientific Reports, 10, 12598.

21. Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al. (2021). Swarm learning for decentralized and confidential clinical machine learning. Nature, 594, 265–270.

22. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology, 10(2), 1–19.