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A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT

Author

Listed:
  • Innocent Boakye Ababio

    (Department of Computer and Information Science, Fordham University, New York, NY 10023, USA
    I. B. Ababio and J. Bieniek contributed equally to this work.)

  • Jan Bieniek

    (Department of Computer and Information Science, Fordham University, New York, NY 10023, USA
    I. B. Ababio and J. Bieniek contributed equally to this work.)

  • Mohamed Rahouti

    (Department of Computer and Information Science, Fordham University, New York, NY 10023, USA)

  • Thaier Hayajneh

    (Department of Computer and Information Science, Fordham University, New York, NY 10023, USA)

  • Mohammed Aledhari

    (Department of Data Science, University of North Texas, Denton, TX 76207, USA)

  • Dinesh C. Verma

    (IBM TJ Watson Research Center, P.O. Box 218, Yorktown Heights, NY 10598, USA)

  • Abdellah Chehri

    (Department of Mathematics and Computer Science, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada)

Abstract

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.

Suggested Citation

  • Innocent Boakye Ababio & Jan Bieniek & Mohamed Rahouti & Thaier Hayajneh & Mohammed Aledhari & Dinesh C. Verma & Abdellah Chehri, 2025. "A Blockchain-Assisted Federated Learning Framework for Secure and Self-Optimizing Digital Twins in Industrial IoT," Future Internet, MDPI, vol. 17(1), pages 1-20, January.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:1:p:13-:d:1559416
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