Boosting IoT Intrusion Detection with Hybrid Federated Learning

Abstract

IoT network security faces dual challenges: effectively detecting intrusions while safeguarding user privacy. This paper introduces HFEL (Hybrid Federated Ensemble Learning), an innovative approach integrating tree-based algorithms with neural networks in a privacy-preserving federated architecture. By combining Random Forest and XGBoost with a Federated Multi-Layer Perceptron, our framework maintains data locality while enhancing detection capabilities through ensemble techniques. HFEL uniquely addresses the performance initialization issues common in federated learning systems through its hybrid design. Experimental validation on BoT-IoT and Edge-IIoT datasets demonstrates HFEL's exceptional performance, achieving detection accuracies of 99.94 % and 97.53 % respectively. Comparative analysis reveals substantial improvements over both traditional federated approaches and current state-of-the-art methods. This research advances the field by demonstrating how ensemble strategies can overcome key limitations in federated intrusion detection without compromising privacy constraints.