Federated Deep Learning for Robust and Scalable Intrusion Detection in the Internet of Medical Things
DOI:
https://doi.org/10.22399/ijcesen.4038Keywords:
Federated Learning, Deep Learning, CNN–LSTM, Intrusion Detection system, Internet of Medical ThingsAbstract
The Internet of Medical Things (IoMT) connects wearable devices, sensors, and healthcare systems to enable continuous patient monitoring and intelligent diagnostics. While offering significant benefits, this connectivity also exposes IoMT to cyberattacks that threaten data integrity and patient safety. Intrusion detection is therefore essential, but traditional centralized methods raise concerns of privacy leakage, high communication cost, and single points of failure. To address these issues, we propose a federated deep learning framework that employs a hybrid Convolutional Neural Network and Long Short-Term Memory CNN–LSTM architecture for intrusion detection. The federated approach allows collaborative model training across distributed clients without sharing raw medical data, preserving privacy while enhancing scalability. Experiments conducted on the CIC-IoMT2024 dataset under both IID and non-IID data distributions demonstrate that the framework achieves up to 99% accuracy in binary classification and strong robustness in multi-class scenarios. These findings confirm that federated deep learning offers a robust and scalable solution for securing IoMT networks while safeguarding sensitive medical information.
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