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EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks

Author

Listed:
  • Sakshi Patni

    (School of Computing, Gachon University, Seongnam 13120, Republic of Korea)

  • Joohyung Lee

    (School of Computing, Gachon University, Seongnam 13120, Republic of Korea)

Abstract

The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare.

Suggested Citation

  • Sakshi Patni & Joohyung Lee, 2024. "EdgeGuard: Decentralized Medical Resource Orchestration via Blockchain-Secured Federated Learning in IoMT Networks," Future Internet, MDPI, vol. 17(1), pages 1-21, December.
  • Handle: RePEc:gam:jftint:v:17:y:2024:i:1:p:2-:d:1553021
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    References listed on IDEAS

    as
    1. Habib Ullah Manzoor & Attia Shabbir & Ao Chen & David Flynn & Ahmed Zoha, 2024. "A Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy," Future Internet, MDPI, vol. 16(10), pages 1-37, October.
    2. Yazan Otoum & Chaosheng Hu & Eyad Haj Said & Amiya Nayak, 2024. "Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration," Future Internet, MDPI, vol. 16(10), pages 1-17, October.
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