Securing Internet of Medical Things: An Advanced Federated Learning Approach

The Internet of Medical Things (IoMT) is transforming healthcare through extensive automation, data collection, and real-time communication among interconnected devices. However, this rapid expansion introduces significant security vulnerabilities that traditional centralized solutions or device-level protections often fail to adequately address due to challenges related to latency, scalability, and resource constraints. This study presents a novel federated learning (FL) framework tailored for IoMT security, incorporating techniques such as stacking, federated dynamic averaging, and active user participation to decentralize and enhance attack classification at the edge. Utilizing the CICIoMT2024 dataset, which encompasses 18 attack classes and 45 features, we deploy Random Forest (RF), AdaBoost, Support Vector Machine (SVM), and Deep Learning (DL) models across 10 simulated edge devices. Our federated approach effectively distributes computational loads, mitigating the strain on central servers and individual devices, thereby enhancing adaptability and resource efficiency within IoMT networks. The RF model achieves the highest accuracy of 99.22%, closely followed by AdaBoost, demonstrating the feasibility of FL for robust and scalable edge security. While this study validates the proposed framework using a single realistic dataset in a controlled environment, future work will explore additional datasets and real-world scenarios to further substantiate the generalization and effectiveness of the approach. This research underscores the potential of federated learning to address the unique security and computational constraints of IoMT, paving the way for practical, decentralized deployments that strengthen device-level defenses across diverse healthcare settings.

@article{Misbah2025,
  title     = {Securing Internet of Medical Things: An Advanced Federated Learning Approach},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Anass Misbah and Anass Sebbar and Imad Hafidi},
  doi       = {10.14569/IJACSA.2025.01602129},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602129}
}