FEDERATED LEARNING FOR PRIVACY-PRESERVING INTRUSION DETECTION IN HETEROGENEOUS IOT NETWORKS
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The rapid proliferation of Internet of Things (IoT) devices across industrial, domestic, and healthcare environments has created significant challenges in network security. Traditional centralised intrusion detection systems (IDS) are inadequate for IoT deployments due to privacy constraints, communication overhead, and the heterogeneous nature of device ecosystems. This paper proposes a federated learning-based intrusion detection framework (FL-IDS) that enables distributed model training across IoT edge nodes without transmitting raw data to a central server. The proposed architecture employs a lightweight convolutional-recurrent neural network (CRNN) at each participating node, with a modified FedAvg aggregation strategy that accounts for data imbalance and device heterogeneity. Experiments conducted on the N-BaIoT and TON-IoT benchmark datasets demonstrate that FL-IDS achieves a detection accuracy of 97.43% and an F1-score of 96.89%, while reducing communication overhead by 61.2% compared to centralised approaches. The framework also demonstrates robustness against Byzantine attacks and adversarial data poisoning, making it suitable for deployment in real-world constrained environments
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Shrimali, J. (2026). Federated Learning for Privacy-Preserving Intrusion Detection in Heterogeneous IOT Networks. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.124
Shrimali, Jay. "Federated Learning for Privacy-Preserving Intrusion Detection in Heterogeneous IOT Networks." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.124.
Shrimali, Jay. "Federated Learning for Privacy-Preserving Intrusion Detection in Heterogeneous IOT Networks." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.124.
References
2.Kolias C., Kambourakis G., Stavrou A., Voas J., "DDoS in the IoT: Mirai and other botnets," Computer, 2017, 50 (7), 80-84.
3.Bace R.G., Mell P., "Intrusion Detection Systems," NIST Special Publication, 2001.
4.McMahan H.B., Moore E., Ramage D., Hampson S., y Arcas B.A., "Communication-efficient learning of deep networks from decentralized data," Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017, 54, 1273-1282.
5.Diro A.A., Chilamkurti N., "Distributed attack detection scheme using deep learning approach for Internet of Things," Future Generation Computer Systems, 2018, 82, 761-768.
6.Preuveneers D., Rimmer V., Tsingenopoulos I., Spooren J., Joosen W., Ilie-Zudor E., "Chained anomaly detection models for federated learning: An intrusion detection case study," Applied Sciences, 2018, 8 (12), 2663.
7.Mothukuri V., Parizi R.M., Pouriyeh S., Huang Y., Dehghantanha A., Srivastava G., "A survey on security and privacy of federated learning," Future Generation Computer Systems, 2021, 115, 619-640.
8.Rahman S.A., Tout H., Talhi C., Mourad A., "Internet of Things intrusion detection: Centralized, on-device, or federated learning?," IEEE Network, 2020, 34 (6), 310-317.
9.Bhagoji A.N., Chakraborty S., Mittal P., Calo S., "Analyzing federated learning through an adversarial lens," Proceedings of the 36th International Conference on Machine Learning (ICML), 2019, 97, 634-643.
10.Blanchard P., El Mhamdi E.M., Guerraoui R., Stainer J., "Machine learning with adversaries: Byzantine tolerant gradient descent," Advances in Neural Information Processing Systems, 2017, 30, 119-129.
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