XAI-SOH-FL: Enhancing SOH-FL with Adaptive Aggregation and Explainable AI for Intrusion Detection in Heterogeneous IoT
Researchers have developed XAI-SOH-FL, a new framework designed to improve intrusion detection in heterogeneous IoT environments. This enhanced system integrates adaptive aggregation and explainable AI to address limitations in existing federated learning approaches. Experiments show XAI-SOH-FL achieves 94.12% accuracy and 0.92 F1-score on the CICIDS2017 dataset, outperforming baseline models while converging faster. AI
IMPACT Enhances security and interpretability for AI-driven intrusion detection in IoT systems.