IJLTEMAS International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) IJLTEMAS 2278-2540 IJLTEMAS 563 10.51583/IJLTEMAS.2026.151000044 Machine Learning An Adaptive Federated Machine Learning Framework for Privacy-Preserving Prediction of Type 2 Diabetes Mellitus across Healthcare Institutions. Olasunkanmi Joshua OLAOYE, Caleb University Lagos, Department of Computer Science. Nigeria Olukayode FOLARIN Olufemi Caleb University Lagos, Department of Computer Science. Nigeria Ademola Saheed Omisore Crescent University, Abeokuta, Ogun State, Department of Computer Science. Nigeria 15 10 542 549 20 10 2026 25 10 2026 03 11 2026 There are challenges in collaboration between health care institutions in the areas of confidentiality governance considerations and information system integration that hinder the accurate prediction of Type 2 diabetes mellitus to enable timely preventive interventions. This is further complicated by differences in the populations of patients number of patients in datasets the computing resources available and participation. In this study a novel adaptive federated machine learning approach for privacy-preserving diabetes onset prediction within three years is proposed. The methodological design calls for 5 000 synthetic records of adult patients across five simulated hospitals. Preprocessing consists of duplicate removal harmonisation of variables missing value imputation categorical encoding numerical scaling and class weighting and is only applied to the training data. Prediction model gets local training aggregation weight based on model validation performance and update reliability and participation history. Differential privacy and secure aggregation are defined as complementary protection measures. Discrimination sensitivity F1 score calibration institutional consistency and computational efficiency are used as evaluation metrics for comparison between local-only centralised FedAvg FedProx and adaptive approaches. Illustrative performance targets are an AUROC of 0.9000 sensitivity of 0.8200 and F1 score of 0.8500. An efficiency illustration shows that the training time is reduced by 25% the volume of communication is reduced by 33.3% and convergence is achieved in 60 rounds when compared to the efficiency illustrated with FedAvg. These numbers are not experimentally validated and differences in institution performance confidence intervals and numerical privacy budgets are not measured. The contribution is an integrated design connecting adaptive collaboration explicit privacy controls and comprehensive evaluation. To draw conclusions on effectiveness and readiness for deployment in different clinical contexts the implementation needs to be reproducible privacy accounting must be done and external clinical validation must be conducted. Abstract

There are challenges in collaboration between health care institutions in the areas of confidentiality, governance considerations and information system integration that hinder the accurate prediction of Type 2 diabetes mellitus to enable timely preventive interventions. This is further complicated by differences in the populations of patients, number of patients in datasets, the computing resources available and participation. In this study, a novel adaptive federated machine learning approach for privacy-preserving diabetes onset prediction within three years is proposed. The methodological design calls for 5,000 synthetic records of adult patients, across five simulated hospitals. Preprocessing consists of duplicate removal, harmonisation of variables, missing value imputation, categorical encoding, numerical scaling, and class weighting, and is only applied to the training data. Prediction model gets local training, aggregation weight based on model validation performance and update reliability and participation history. Differential privacy and secure aggregation are defined as complementary protection measures. Discrimination, sensitivity, F1 score, calibration, institutional consistency, and computational efficiency are used as evaluation metrics for comparison between local-only, centralised, FedAvg, FedProx, and adaptive approaches. Illustrative performance targets are an AUROC of 0.9000, sensitivity of 0.8200, and F1 score of 0.8500. An efficiency illustration shows that the training time is reduced by 25%, the volume of communication is reduced by 33.3%, and convergence is achieved in 60 rounds, when compared to the efficiency illustrated with FedAvg. These numbers are not experimentally validated and differences in institution performance, confidence intervals and numerical privacy budgets are not measured. The contribution is an integrated design connecting adaptive collaboration, explicit privacy controls, and comprehensive evaluation. To draw conclusions on effectiveness and readiness for deployment in different clinical contexts, the implementation needs to be reproducible, privacy accounting must be done, and external clinical validation must be conducted.

 References Aagaard, A., Röttger, R., Johnson, E. K., & Olsen, K. R. (2025). Comparing the predictive performance of diabetes complications using administrative health data and clinical data. Scientific Reports, 15, Article 33035. https://doi.org/10.1038/s41598-025-18079-7 Arkan Yurtoglu, R., Dervişoğlu, H., Aktaş, M., Daşçılar, M., Halepmollası, R., Baz, İ., & Haklidir, M. (2024, November). Federated learning for diabetes prediction in healthcare [Conference presentation]. IV International Congress on Artificial Intelligence in Health, Izmir, Türkiye. Deng, Y., Kamani, M. M., & Mahdavi, M. (2020). Adaptive personalized federated learning [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2003.13461 Dhade, P., & Shirke, P. (2023). Federated learning for healthcare: A comprehensive review. Engineering Proceedings, 59(1), Article 230. https://doi.org/10.3390/engproc2023059230 Goyal, R., Singhal, M., & Jialal, I. (2023). Type 2 diabetes. In StatPearls. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK513253/ Herath, H. M. S. S., Herath, H. M. K. K. M. B., Madhusanka, B. G. D. A., & Guruge, L. G. P. K. (2024). Data protection challenges in the processing of sensitive data. In C. Hewage, L. Yasakethu, & D. N. K. Jayakody (Eds.), Data protection (pp. 155–179). Springer. https://doi.org/10.1007/978-3-031-76473-8_8 Horst, A., Loustalot, P., Yoganathan, S., Li, T., Xu, J., Tong, W., Schneider, D., Löffler-Perez, N., Di Renzo, E., & Renaudin, M. (2025). Federated learning: A privacy-preserving approach to data-centric regulatory cooperation. Frontiers in Drug Safety and Regulation, 5, Article 1579922. https://doi.org/10.3389/fdsfr.2025.1579922 Jayalakshmi, R., & Tamilvizhi, T. (2025). Privacy preservation in diabetic disease prediction using federated learning based on efficient cross stage recurrent model. Scientific Reports, 15, Article 37258. https://doi.org/10.1038/s41598-025-21229-6 Kawalkar, U., Gaidhane, A., Aggarwal, S., Mankar, A., Ambadekar, N., & Kogade, P. (2025). Challenges and strategies for early diagnosis and management of type 2 diabetes mellitus at the primary healthcare level in rural Western Vidarbha, India: A qualitative study. BMJ Public Health, 3(2), Article e003126. https://doi.org/10.1136/bmjph-2025-003126 Koch, A., Brust, L., & Weigl, M. (2024). Barriers and facilitators of patient engagement activities to improve patient safety in healthcare organizations: A Delphi-based expert survey. Risk Management and Healthcare Policy, 17, 3217–3226. https://doi.org/10.2147/RMHP.S489522 Ravi, R., & Olickal, J. J. (2026). Machine learning-based risk prediction models for type 2 diabetes in primary care: A scoping review. Frontiers in Public Health, 14, Article 1946070. https://doi.org/10.3389/fpubh.2026.1946070 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, Article 119. https://doi.org/10.1038/s41746-020-00323-1 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, Article 12598. https://doi.org/10.1038/s41598-020-69250-1 SM, M. (2025). Diabetes prediction using federated learning. International Journal of Scientific Research in Engineering and Management, 9(1), 1–9. https://doi.org/10.55041/IJSREM41133 Teo, Z. L., Jin, L., Liu, N., Li, S., Miao, D., Zhang, X., Ng, W. Y., Tan, T. F., Lee, D. M., Chua, K. J., Heng, J., Liu, Y., Goh, R. S. M., & Ting, D. S. W. (2024). Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture. Cell Reports Medicine, 5(2), Article 101419. https://doi.org/10.1016/j.xcrm.2024.101419 Usman, M., Bernardi, M. L., & Cimitile, M. (2025). Introducing a quality-driven approach for federated learning. Sensors, 25(10), Article 3083. https://doi.org/10.3390/s25103083 Wang, Q., Cheng, H., Jiang, S., Zhang, L., Liu, X., Chen, P., Liu, J., Li, Y., Liu, X., Wang, L., Li, Z., Cai, G., Chen, X., & Dong, Z. (2024). The relationship between diabetic retinopathy and diabetic nephropathy in type 2 diabetes. Frontiers in Endocrinology, 15, Article 1292412. https://doi.org/10.3389/fendo.2024.1292412 Yang, X., Yao, M., Huang, J., Cheng, Z., & Sun, T. (2026). Machine learning predicts diabetes risk in high-risk populations: Analysis of National Health and Nutrition Examination Survey data. Archives of Medical Science, 22(2), 708–720. https://doi.org/10.5114/aoms/209547 Corrections: Yang et al. uses 2026, the year of its assigned journal issue, rather than its 2025 online publication date. Issue 2/2026 vol. 22 Dhade and Shirke uses 2023, following the publisher’s recommended citation. The Manjunath entry retains “SM” as the indexed surname; confirm the name order against the original article before submission.