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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Sumit Kumar Banchhor
Amity University Chhattisgarh
Computer Science & IT

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Federated deep learning for early detection and progression modelling of diabetic nephropathy Aravind Kumar Kyrika; Sumit Kumar Banchhor
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 4: December 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i4.pp1453-1458

Abstract

One of the main causes of end-stage renal disease is diabetic nephropathy (DN). Patient privacy-sensitive data is a restriction in centralized machine-learning approaches for the healthcare domain. This research introduces a privacy-preserving federated deep-learning (Fed-DN) method for early diagnosis of DN and modelling disease progression and its impact. Fed-DN combines safe aggregation with differential privacy (DP) using Opacus and multi-modal data sources (electronic health records, fundus imaging, and histopathology). Both simulation tests with multi-modal data sets distributed across hospitals show an efficient privacy-utility trade-off, with Fed-DN achieving near-centralized performance (AUC ≈ 0.90) (Accuracy = 87%) while maintaining a privacy budget of ε ≈ 3.1. This is a good example of the trade-off achieved between model accuracy and privacy protection. The technique does not reveal patient information, which enables collaborative, scalable healthcare analytics. Furthermore, it enhances trust among patients and institutions.
Co-Authors Aravind Kumar Kyrika