{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T07:01:14Z","timestamp":1790233274751,"version":"4.1.0"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100031729","name":"Gulf University for Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031729","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Artificial intelligence is increasingly used in connected medical-device systems for physiological-signal classification and continuous monitoring. This paper presents a simulation study of the learning and policy layers of a zero-trust federated-learning framework for Internet of Medical Things (IoMT) devices. The implemented prototype combines federated averaging, Gaussian perturbation of client updates, a device-level trust score, and gradient-magnitude screening of anomalous client updates. This is a simulation study of the federated-learning and zero-trust policy layers; cryptographic, encrypted-transport, and hardware-attestation components are modeled as deployment considerations or overheads and are not implemented as complete physical or protocol-level modules. Using the ECG classification pipeline described in the methodology, we compare centralized training, traditional federated learning, differentially private federated learning, and the proposed zero-trust federated configuration. The proposed ZT-FL configuration is reported at 0.928 accuracy, 0.920 F1-score, and 0.968 AUC. These results support feasibility within the stated simulation scope and do not establish clinical validity, formal end-to-end DP accounting, comprehensive adversarial robustness, regulatory compliance, or post-quantum readiness.<\/jats:p>","DOI":"10.3389\/fdgth.2026.1807342","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:28:48Z","timestamp":1790231328000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A simulation study of differentially private federated learning with zero-trust policy screening for medical IoT devices"],"prefix":"10.3389","volume":"8","author":[{"given":"Ihssan S.","family":"Masad","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Gulf University for Science and Technology (GUST)","place":["Hawally, Kuwait"]},{"name":"Department of Biomedical Systems and Informatics Engineering, Yarmouk University","place":["Irbid, Jordan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali Mohammad","family":"Alqudah","sequence":"additional","affiliation":[{"name":"Lucidya Tech LTD","place":["Winnipeg, MB, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoroq","family":"Qazan","sequence":"additional","affiliation":[{"name":"Lucidya Tech LTD","place":["Winnipeg, MB, Canada"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sultan","family":"Shoaib","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Gulf University for Science and Technology (GUST)","place":["Hawally, Kuwait"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2024.101419","article-title":"Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture","volume":"5","author":"Teo","year":"","journal-title":"Cell Reports Medicine"},{"key":"B2","doi-asserted-by":"publisher","first-page":"103497","DOI":"10.1016\/j.media.2025.103497","article-title":"From challenges and pitfalls to recommendations and opportunities: implementing federated learning in healthcare","volume":"101","author":"Li","year":"","journal-title":"Medical image analysis"},{"key":"B3","doi-asserted-by":"publisher","first-page":"2587","DOI":"10.3390\/healthcare12242587","article-title":"Federated learning in smart healthcare","volume":"12","author":"Abbas","year":"2024","journal-title":"Healthcare"},{"key":"B4","doi-asserted-by":"publisher","first-page":"3317","DOI":"10.3390\/su15043317","article-title":"Internet of medical things privacy and security","volume":"15","author":"Kamalov","year":"2023","journal-title":"Sustainability"},{"key":"B5","doi-asserted-by":"publisher","first-page":"3426","DOI":"10.1016\/j.procs.2025.04.599","article-title":"Investigation of security attacks in iomt devices and federated learning as a mitigation strategy","volume":"258","author":"Kiruthika","year":"2025","journal-title":"Procedia Computer Science"},{"key":"B6","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"54","author":"McMahan","year":"2017","journal-title":"Proc 20th Int Conf Artif Intell Statist (AISTATS)"},{"key":"B7","first-page":"374","article-title":"Towards federated learning at scale: system design","author":"Bonawitz","year":"2019"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Found. 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