Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.30471

Enhanced Diabetes Detection via a Privacy-Preserving Federated Learning Framework

Nouhaila Aasoum ORCID...1, Ismail Jellouli ORCID...1, Souad Amjad ORCID...2
1 Emerging Computer Technologies Research Unit, Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco
2 Information Technology and Systems Modeling Laboratory, Faculty of Science, Abdelmalek Essaadi University, Tetouan, Morocco

Background: The integration of artificial intelligence (AI) in healthcare depends on striking a balance between patient privacy and clinical utility. The standard methods often compromise one for the other, preventing the development of trustworthy healthcare AI.

Objective: This paper aims to resolve the privacy-utility trade-off by developing an enhanced federated learning framework with adaptive differential privacy (DP) mechanisms that are optimized for clinical data.

Methods: We implement and compare several different methods, from the most centralized deep learning to various federated configurations with formal DP guarantees. Our improved framework involves adaptive noise scheduling and quality-weighted federated averaging on top of a federated neural network framework. We validate on two major diabetes screening datasets: Diabetes Health Indicators (BRFSS 2015) and National Health and Nutrition Examination Survey (NHANES 2015–2016), including comprehensive clinical measurements.

Results: This paper presents a favourable balance between privacy protection and clinical utility for both datasets. It offers strong formal differential privacy guarantees and good diagnostic performance, achieving high ranking accuracy with clinical risk prioritization. The model demonstrates generalization robustness by capturing clinically meaningful risk factors aligned with established medical guidelines, confirming that the applied privacy-preserving mechanisms do not compromise clinical relevance.

Conclusion: Our framework meaningfully advances the privacy-utility trade-off healthcare AI, by offering tunable formal privacy guarantees while ensuring strong clinical performance. The approach is highly generalizable across diverse data collection methodologies and maintains clinically relevant feature representations, thus allowing safe adoption in sensitive medical domains.

Keywords: Differential privacy; Federated learning; Clinical machine learning; Privacy; Healthcare analytics.

Received: October 25, 2025; Revised: December 24, 2025; Accepted: January 26, 2026; Prepublished online: April 12, 2026 

Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Aasoum, N., Jellouli, I., & Amjad, S. (2021). A Critical Review on Concept Drift Monitoring Process for Class Imbalance in Data Streams Nouhaila. In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning BML, (pp. 404-408). ScitePress. https://doi.org/10.5220/0010735500003101 Go to original source...
    2. Aasoum, N., Jellouli, I., Amjad, S., & Mohammed, Y. M. A. (2024). Security and Privacy-Preserving Techniques of Federated Learning in Edge Computing: A Comparative Study. In 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology. IEEE. https://doi.org/10.1109/IRASET60544.2024.10549292 Go to original source...
    3. Abadi, M., McMahan, H. B., Chu, A., Mironov, I., Zhang, L., Goodfellow, I., & Talwar, K. (2016). Deep learning with differential privacy. In Proceedings of the ACM Conference on Computer and Communications Security, (pp. 308-318). ACM. https://doi.org/10.1145/2976749.2978318 Go to original source...
    4. Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024). Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy , Security , and Predictive Analytics with IoT Integration. Healthcare, 12(24), 2587. https://doi.org/10.3390/healthcare12242587 Go to original source...
    5. Amanullah, S. I., & Solms, S. von. (2025). Balancing Privacy and Performance: A Review of Differential Privacy in Deep and Federated Learning. Journal of Information Systems Engineering and Management, 10, 384-397. https://doi.org/10.52783/jisem.v10i58s.12610 Go to original source...
    6. Bagdasaryan, E., Poursaeed, O., & Shmatikov, V. (2019). Differential privacy has disparate impact on model accuracy. In Proceedings of the 33rd International Conference on Neural Information Processing Systems, (pp. 15479-15488). ACM.
    7. Brisimi, T. S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I. C., & Shi, W. (2018). Federated learning of predictive models from federated Electronic Health Records. International Journal of Medical Informatics, 112, 59-67. https://doi.org/10.1016/j.ijmedinf.2018.01.007 Go to original source...
    8. Chawla, S., Dwork, C., McSherry, F., Smith, A., & Wee, H. (2005). Toward privacy in public databases. In Theory of Cryptography - Lecture Notes in Computer Science, (pp. 363-385). Springer. https://doi.org/10.1007/978-3-540-30576-7_20 Go to original source...
    9. Cheng, Y., Li, W., Qin, S., & Tu, T. (2025). Differential privacy federated learning based on adaptive adjustment. Computers, Materials & Continua/Computers, Materials & Continua, 82(3), 4777-4795. https://doi.org/10.32604/cmc.2025.060380 Go to original source...
    10. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3-4), 211-487. https://doi.org/10.1561/0400000042 Go to original source...
    11. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. https://doi.org/10.1038/s41591-018-0316-z Go to original source...
    12. Huang, L., Shea, A. L., Qian, H., Masurkar, A., Deng, H., & Liu, D. (2019). Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. Journal of Biomedical Informatics, 99(September), 103291. https://doi.org/10.1016/j.jbi.2019.103291 Go to original source...
    13. Jimenez Gutierrez, D. M., Hassan, H. M., Landi, L., Vitaletti, A., & Chatzigiannakis, I. (2024). Application of Federated Learning Techniques for Arrhythmia Classification Using 12-Lead ECG Signals. In Algorithmic Aspects of Cloud Computing, (pp. 38-65). Springer. https://doi.org/10.1007/978-3-031-49361-4_3 Go to original source...
    14. 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(6), 473-484. https://doi.org/10.1038/s42256-021-00337-8 Go to original source...
    15. Kumar, A. V., Sujith, M. S., Sai, K. T., Rajesh, G., & Yashwanth, D. J. S. (2020). Secure Multiparty computation enabled E-Healthcare system with Homomorphic encryption. In International Conference on Recent Advancements in Engineering and Management, 981(2), 022079. https://doi.org/10.1088/1757-899X/981/2/022079 Go to original source...
    16. 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. In Machine Learning in Medical Imaging, (pp. 133-141). Springer. https://doi.org/10.1007/978-3-030-32692-0_16 Go to original source...
    17. McDonagh, T. A., Metra, M., Adamo, M., Baumbach, A., Böhm, M., Burri, H., Èelutkiene, J., Chioncel, O., Cleland, J. G. F., Coats, A. J. S., Crespo-Leiro, M. G., Farmakis, D., Gardner, R. S., Gilard, M., Heymans, S., Hoes, A. W., Jaarsma, T., Jankowska, E. A., Lainscak, M., … Koskinas, K. C. (2021). 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) With the special contribution of the Heart Failure Association (HFA) of the ESC. European Heart Journal, 42(36), 3599-3726. https://doi.org/10.1093/eurheartj/ehab368 Go to original source...
    18. Peterson, D., Kanani, P., & Marathe, V. J. (2019). Private Federated Learning with Domain Adaptation. arXiv:1912.06733. http://arxiv.org/abs/1912.06733
    19. Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37-43. https://doi.org/10.1038/s41591-018-0272-7 Go to original source...
    20. Rogers, R., Samorodnitsky, G., Wu, Z. S., & Ramdas, A. (2023). Adaptive Privacy Composition for Accuracy-first Mechanisms. In Proceedings of the 37th International Conference on Neural Information Processing Systems, (pp. 15833-15854). ACM. Go to original source...
    21. Scheibner, J., Raisaro, J. L., Troncoso-Pastoriza, J. R., Ienca, M., Fellay, J., Vayena, E., & Hubaux, J. P. (2021). Revolutionizing medical data sharing using advanced privacy-enhancing technologies: Technical, legal, and ethical synthesis. Journal of Medical Internet Research, 23(2), e25120. https://doi.org/10.2196/25120 Go to original source...
    22. 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(1), 12598. https://doi.org/10.1038/s41598-020-69250-1 Go to original source...
    23. Suriyakumar, V. M., Papernot, N., Goldenberg, A., & Ghassemi, M. (2021). Chasing your long tails: Differentially private prediction in health care settings. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, (pp. 723-734). ACM. https://doi.org/10.1145/3442188.3445934 Go to original source...
    24. Talaei, M., & Izadi, I. (2024). Adaptive Differential Privacy in Federated Learning: A Priority-Based Approach. arXiv:2401.02453. https://doi.org/10.48550/arXiv.2401.02453 Go to original source...
    25. Upreti, D., Yang, E., Kim, H., & Seo, C. (2024). A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications. Computer Modeling in Engineering and Sciences, 140(3), 2239-2274. https://doi.org/10.32604/cmes.2024.048932 Go to original source...
    26. Vaidya, J., Anirban, B., Basit, S., & Hong, Y. (2013). Differentially Private Naive Bayes Classification. In IEEE WIC ACM International Conference on Web Intelligence. IEEE. https://doi.org/10.1109/WI-IAT.2013.80 Go to original source...
    27. Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689 Go to original source...
    28. Williamson, S. M., & Prybutok, V. (2024). Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare. Applied Sciences, 14(2), 675. https://doi.org/10.3390/app14020675 Go to original source...
    29. Ziller, A., Usynin, D., Braren, R., Makowski, M., Rueckert, D., & Kaissis, G. (2021). Medical imaging deep learning with differential privacy. Scientific Reports, 11(1), 13524. https://doi.org/10.1038/s41598-021-93030-0 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return