A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT
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 prese...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/13 |
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| author | Innocent Boakye Ababio Jan Bieniek Mohamed Rahouti Thaier Hayajneh Mohammed Aledhari Dinesh C. Verma Abdellah Chehri |
| author_facet | Innocent Boakye Ababio Jan Bieniek Mohamed Rahouti Thaier Hayajneh Mohammed Aledhari Dinesh C. Verma Abdellah Chehri |
| author_sort | Innocent Boakye Ababio |
| collection | DOAJ |
| description | 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’s effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations. |
| format | Article |
| id | doaj-art-1e15fa81b80c49b2a9aba57e6a29f097 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-1e15fa81b80c49b2a9aba57e6a29f0972025-01-24T13:33:33ZengMDPI AGFuture Internet1999-59032025-01-011711310.3390/fi17010013A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoTInnocent Boakye Ababio0Jan Bieniek1Mohamed Rahouti2Thaier Hayajneh3Mohammed Aledhari4Dinesh C. Verma5Abdellah Chehri6Department of Computer and Information Science, Fordham University, New York, NY 10023, USADepartment of Computer and Information Science, Fordham University, New York, NY 10023, USADepartment of Computer and Information Science, Fordham University, New York, NY 10023, USADepartment of Computer and Information Science, Fordham University, New York, NY 10023, USADepartment of Data Science, University of North Texas, Denton, TX 76207, USAIBM TJ Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USADepartment of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, CanadaOptimizing 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’s effectiveness in enhancing security, explainability, and optimization, offering improved efficiency and reliability for industrial operations.https://www.mdpi.com/1999-5903/17/1/13blockchaindigital twinsfederated learningIndustrial Internet of Things |
| spellingShingle | Innocent Boakye Ababio Jan Bieniek Mohamed Rahouti Thaier Hayajneh Mohammed Aledhari Dinesh C. Verma Abdellah Chehri A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT Future Internet blockchain digital twins federated learning Industrial Internet of Things |
| title | A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT |
| title_full | A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT |
| title_fullStr | A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT |
| title_full_unstemmed | A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT |
| title_short | A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT |
| title_sort | blockchain assisted federated learning framework for secure and self optimizing digital twins in industrial iot |
| topic | blockchain digital twins federated learning Industrial Internet of Things |
| url | https://www.mdpi.com/1999-5903/17/1/13 |
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