Mist-Assisted Federated Learning for Intrusion Detection in Heterogeneous IoT Networks

The rapid growth of the Internet of Things (IoT) offers new opportunities but also expands the attack surface of distributed, resource-limited devices. Intrusion detection in such environments is difficult due to data heterogeneity from diverse sensing modalities and the non-IID distribution of samples across clients. Federated Learning (FL) provides a privacy-preserving alternative to centralized training, yet conventional frameworks struggle under these conditions. To address this, we propose a Mist-assisted hierarchical framework for IoT intrusion detection. A four-layer Mist-Edge-Fog-Cloud architecture is proposed to mitigate heterogeneity and preserve privacy. Evaluations on the TON-IoT dataset show the framework achieves 98-99% accuracy, PR-AUC $>0.97$, and stable convergence under heterogeneous and large-scale settings, while maintaining efficiency and preserving privacy.