Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) for IoT Intrusion Detection

Abstract & Keywords

The rapid expansion of the Internet of Things (IoT) across critical sectors has increased the risk of cyberattacks, making secure intrusion detection essential. This paper proposes a Privacy-Preserving Hierarchical Fog Federated Learning (PP-HFFL) framework for IoT intrusion detection. In the proposed architecture, fog nodes act as intermediaries between IoT devices and the cloud, collecting and preprocessing local data while performing federated model training. The framework integrates Personalized Federated Learning (PFL) to effectively handle heterogeneous non-IID data and incorporates Differential Privacy (DP) to preserve sensitive information during model updates. Experimental evaluation on the RT-IoT 2022 and CIC-IoT 2023 datasets demonstrates that PP-HFFL achieves detection accuracy comparable to centralized approaches while significantly improving privacy protection, scalability, communication efficiency, and adaptability in real-world IoT environments. The proposed framework provides a practical and secure solution for next-generation IoT intrusion detection systems. Keywords

Author Affiliations

References Listing (3)

1. [1]K. R. Rao and J. Doe, "High-resolution convolutional modeling in thermal imaging systems," IEEE Trans. Image Process., vol. 31, pp. 210–222, Jan. 2024.
2. [2]W. Liu, R. Chen, and M. Rossi, "Deep residual neural networks for noise-reduction and super-resolution edge rebuilding," Pattern Recognition, vol. 142, pp. 110-125, Oct. 2025.
3. [3]S. Jenkins, "Cooperative token-heuristics inside warehousing routing grids," Int. J. Rob. Res., vol. 18, no. 4, pp. 450–467, May 2025.