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This paper is in the following e-collection/theme issue:

Artificial Intelligence in Diabetes Care and Prevention (114) Ethics, Privacy, and Legal Issues (825) Clinical Informatics (2342) Clinical Information and Decision Making (3816) Tools, Programs and Algorithms (594) Health Information Exchange (235) Blockchain, Distributed Ledger Apps for Health and Medicine (152)

Published on in Vol 11 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79166, first published .
Woman checking blood glucose level with a meter

Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework

Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework

Authors of this article:

Md Rakibul Hasan1 Author Orcid Image ;   Juan Li1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Sowan B, Massad B. Federated learning in obstetrics and gynaecology: A systematic review of applications, architectures, privacy frameworks, and clinical translation challenges. Informatics in Medicine Unlocked 2026;65:101794 View
  2. Sun Y, Yang R, Xin Q, Luo X, Zhou Y, Fu Z, Li J, Gu Z, Liu J, Wang Q, Yang Y. Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management. Frontiers in Endocrinology 2026;17 View