{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T23:15:48Z","timestamp":1776381348932,"version":"3.51.2"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Optimizing digital twins in the Industrial Internet of Things (IIoT) requires secure and adaptable AI models. The IIoT enables digital twins, virtual replicas of physical assets, to improve real-time decision-making, but challenges remain in trust, data security, and model accuracy. This paper presents a novel framework combining blockchain technology and federated learning (FL) to address these issues. By deploying AI models on edge devices and using FL, data privacy is maintained while enabling collaboration across industrial assets. Blockchain ensures secure data management and transparency, while explainable AI (XAI) enhances interpretability. The framework improves transparency, control, security, privacy, and scalability for self-optimizing digital twins in IIoT. A real-world evaluation demonstrates the framework\u2019s effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations.<\/jats:p>","DOI":"10.3390\/fi17010013","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T05:02:02Z","timestamp":1735880522000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT"],"prefix":"10.3390","volume":"17","author":[{"given":"Innocent Boakye","family":"Ababio","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, New York, NY 10023, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4046-2472","authenticated-orcid":false,"given":"Jan","family":"Bieniek","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, New York, NY 10023, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9701-5505","authenticated-orcid":false,"given":"Mohamed","family":"Rahouti","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, New York, NY 10023, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8952-1499","authenticated-orcid":false,"given":"Thaier","family":"Hayajneh","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Fordham University, New York, NY 10023, USA"}]},{"given":"Mohammed","family":"Aledhari","sequence":"additional","affiliation":[{"name":"Department of Data Science, University of North Texas, Denton, TX 76207, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1933-7343","authenticated-orcid":false,"given":"Dinesh C.","family":"Verma","sequence":"additional","affiliation":[{"name":"IBM TJ Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4193-6062","authenticated-orcid":false,"given":"Abdellah","family":"Chehri","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12861","DOI":"10.1109\/JIOT.2021.3139827","article-title":"The duo of artificial intelligence and big data for industry 4.0: Applications, techniques, challenges, and future research directions","volume":"9","author":"Jagatheesaperumal","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MITP.2022.3225064","article-title":"Building digital twins of cyber physical systems with metaverse for industry 5.0 and beyond","volume":"24","author":"Jagatheesaperumal","year":"2022","journal-title":"IT Prof."},{"key":"ref_3","first-page":"3","article-title":"Developments of Digital Twin Technologies in Industrial, Smart City, and Healthcare Sectors: A Survey","volume":"1","author":"Yang","year":"2021","journal-title":"Complex Eng. 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