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<p>The Internet of Medical Things (IoMT) empowers healthcare professionals with valuable insights, customized treatments, and remote patient monitoring by enabling real-time monitoring, data collection, and analysis. In IoMT networks, centralized servers are commonly used for aggregating IoT data, but this approach poses security and privacy risks. A strong security system and decentralized data management methods must be implemented to protect sensitive medical information and mitigate cyber threats. This paper presents an innovative SBFL-GAN framework that creates a two-tier security framework for the Internet of Medical Things (IoMT): The first tier uses blockchain technology, strengthened with Paillier encryption and smart contracts, to ensure the security of data transactions and storage by decentralizing them and creating permanent records. The second tier utilizes Federated Learning and Generative Adversarial Networks (GANs) to facilitate cooperative model training among various entities without sharing raw data. Performance simulations were compared with relevant reference models, indicating that the proposed framework outperforms existing frameworks.</p>

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SBFL-GAN: secure blockchain empowered federated learning and GAN model for providing preserving of IoMT

  • 期刊论文

  • 发表日期：

    2026年1月23日

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  • Alyaa A. Hamza,
  • Hesham A. Sakr,
  • Abdelgwad Elashry

摘要

The Internet of Medical Things (IoMT) empowers healthcare professionals with valuable insights, customized treatments, and remote patient monitoring by enabling real-time monitoring, data collection, and analysis. In IoMT networks, centralized servers are commonly used for aggregating IoT data, but this approach poses security and privacy risks. A strong security system and decentralized data management methods must be implemented to protect sensitive medical information and mitigate cyber threats. This paper presents an innovative SBFL-GAN framework that creates a two-tier security framework for the Internet of Medical Things (IoMT): The first tier uses blockchain technology, strengthened with Paillier encryption and smart contracts, to ensure the security of data transactions and storage by decentralizing them and creating permanent records. The second tier utilizes Federated Learning and Generative Adversarial Networks (GANs) to facilitate cooperative model training among various entities without sharing raw data. Performance simulations were compared with relevant reference models, indicating that the proposed framework outperforms existing frameworks.