Privacy-preserving intrusion detection system using TabTransformer and XGboost for IoMT environments

The concept of Internet of Medical Things (IoMT) has improved healthcare delivery by enabling continuous patient monitoring. The inherent nature of IoMT (e.g., heterogeneous and privacy-sensitive) introduces significant security vulnerabilities. Traditional intrusion detection approaches face limitations in scalability and data privacy. To address these challenges, a novel three-phase heterogeneous Federated Intrusion Detection System (FIDS) is presented that integrates federated representation learning and gradient-boosted decision trees in a privacy-aware ensemble framework. The proposed framework is evaluated on the CIC-IoMT 2024 WiFi/MQTT dataset (8.775 million samples, 45 features, 4 attack families) under stratified cross-validation. The proposed framework achieves an accuracy of 99.89%, macro-averaged F1-score of 0.9568, and Matthews Correlation Coefficient (MCC) of 0.9869. These results show the effectiveness of heterogeneous federated model fusion in privacy-preserving IoMT security.