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<p>The advancement of cutting-edge technologies such as the Internet of Things (IoT) and Deep Learning (DL) has transformed the Internet of Medical Things (IoMT) based healthcare into a new paradigm known as the Healthcare 5.0. This paradigm shift, particularly within Healthcare 5.0, introduces smart, cost-effective, and sustainable healthcare services. However, in such complex and heterogeneous IoMT based networks, smart devices generate large volumes of imbalance data. Most DL models struggle to accurately distinguish malicious behavior and, consequently, fail to detect network threats effectively. To address these challenges and ensure privacy preservation, we propose a novel Generative Adversarial Network (GAN)-oriented Federated Learning (FL) model. The proposed approach generates realistic synthetic data for improving the detection of minority-class threats, while FL facilitates distributed training without revealing raw data. Additionally, a Bidirectional Long Short-Term Memory (BiLSTM) network is employed to identify various attack types within smart IoMT-based Healthcare 5.0 systems. Experimental results on two benchmark imbalance datasets, UNSW-NB15 and NSL-KDD, demonstrate that the proposed model achieves superior accuracy of (94.78% and 95.90%) and F1-score (94.88% and 98.70%) respectively for minority-class attacks, outperforming existing methods.</p>

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F-BEGAN: BEGAN-enabled federated learning for imbalance data security and privacy-preserving of IoMT-based Healthcare 5.0

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Zabeeh Ullah,
  • Weiwei Jiang,
  • Abdulrahman Ahmed Gharawi,
  • Mohammad D. Alahmadi

摘要

The advancement of cutting-edge technologies such as the Internet of Things (IoT) and Deep Learning (DL) has transformed the Internet of Medical Things (IoMT) based healthcare into a new paradigm known as the Healthcare 5.0. This paradigm shift, particularly within Healthcare 5.0, introduces smart, cost-effective, and sustainable healthcare services. However, in such complex and heterogeneous IoMT based networks, smart devices generate large volumes of imbalance data. Most DL models struggle to accurately distinguish malicious behavior and, consequently, fail to detect network threats effectively. To address these challenges and ensure privacy preservation, we propose a novel Generative Adversarial Network (GAN)-oriented Federated Learning (FL) model. The proposed approach generates realistic synthetic data for improving the detection of minority-class threats, while FL facilitates distributed training without revealing raw data. Additionally, a Bidirectional Long Short-Term Memory (BiLSTM) network is employed to identify various attack types within smart IoMT-based Healthcare 5.0 systems. Experimental results on two benchmark imbalance datasets, UNSW-NB15 and NSL-KDD, demonstrate that the proposed model achieves superior accuracy of (94.78% and 95.90%) and F1-score (94.88% and 98.70%) respectively for minority-class attacks, outperforming existing methods.