Federated Learning with Explainable AI for Malicious Traffic Detection in IoT Networks Article Swipe
Muhammad Ahmad Bilal
,
Ihtesham Ul Islam
,
Naima Iltaf
,
Muhammad Junaid Khan
,
Muhammad Jaleed Khan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3613459
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3613459
The rapid proliferation of Internet of Things (IoT) devices has intensified the need for scalable, privacy-preserving, and interpretable intrusion detection systems (IDS). In this work, we propose a novel framework that combines Federated Learning (FL) with Explainable Artificial Intelligence (XAI) to detect malicious traffic across distributed IoT networks without centralizing sensitive data. Our IDS model employs a lightweight deep neural network trained collaboratively using FedAvg. SHAP provides global and local attributions; LIME supplies instance-level attributions complementary to SHAP. Experiments conducted on three benchmark datasets: Edge-IIoT, CIC-IoT2023, and TII-SSRC-23, demonstrated that our approach achieves 99.3%, 99.5% and 99.0% accuracy in binary classification, respectively and 97.2%, 98.0% and 96.5% accuracy in multi-class scenarios, respectively, closely matching centralized models while preserving data locality and data privacy. Moreover, the integrated XAI methods enhance the model’s transparency by identifying key traffic features contributing to each alert. These results establish FL-XAI as a practical, interpretable, and privacy-respecting IDS for real-world IoT deployments.
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Federated Learning with Explainable AI for Malicious Traffic Detection in IoT NetworksWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
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Muhammad Ahmad Bilal, Ihtesham Ul Islam, Naima Iltaf, Muhammad Junaid Khan, Muhammad Jaleed KhanList of authors in order
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https://doi.org/10.1109/access.2025.3613459Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3613459Direct OA link when available
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