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<p>The increasing adoption of Industrial Internet of Things (IIoT) devices introduces significant cybersecurity and privacy challenges, particularly anomaly detection and secure data sharing. This study presents SecuFL-IoT, a secure and communication-efficient federated learning framework designed for IIoT environments. SecuFL-IoT integrates adaptive anomaly detection, lattice-based homomorphic encryption, differential privacy, and reinforcement learning-based threshold adjustment to enhance security, privacy, and efficiency. The proposed model is evaluated against state-of-the-art federated learning approaches, including FedAvg, FedProx, and SCAFFOLD, using the X-IIoTID dataset. Experimental results demonstrate that SecuFL-IoT achieves an F1-score of 88.5% and a false positive rate of 2.7%, outperforming baseline models in anomaly detection accuracy. The framework reduces communication overhead by 53%, converges 23% faster than FedOPT, and lowers energy consumption by 35%, making it highly suitable for resource-constrained IIoT devices. Additionally, SecuFL-IoT ensures strong privacy guarantees (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\epsilon=0.9\)</EquationSource> </InlineEquation>) and improves adversarial robustness, reducing data poisoning success rates below 9%. However, the framework introduces encryption latency and assumes a static network topology, which may affect real-time adaptability in highly dynamic environments. In conclusion, SecuFL-IoT provides a scalable, privacy-preserving, and industry-compliant federated learning solution that aligns with ISA/IEC 62,443 cybersecurity standards, ensuring secure anomaly detection in smart factories, power grids, and other critical IIoT infrastructures.</p>

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SecuFL-IoT: an adaptive privacy-preserving federated learning framework for anomaly detection in smart industrial networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月29日

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  • Ali Alqazzaz

摘要

The increasing adoption of Industrial Internet of Things (IIoT) devices introduces significant cybersecurity and privacy challenges, particularly anomaly detection and secure data sharing. This study presents SecuFL-IoT, a secure and communication-efficient federated learning framework designed for IIoT environments. SecuFL-IoT integrates adaptive anomaly detection, lattice-based homomorphic encryption, differential privacy, and reinforcement learning-based threshold adjustment to enhance security, privacy, and efficiency. The proposed model is evaluated against state-of-the-art federated learning approaches, including FedAvg, FedProx, and SCAFFOLD, using the X-IIoTID dataset. Experimental results demonstrate that SecuFL-IoT achieves an F1-score of 88.5% and a false positive rate of 2.7%, outperforming baseline models in anomaly detection accuracy. The framework reduces communication overhead by 53%, converges 23% faster than FedOPT, and lowers energy consumption by 35%, making it highly suitable for resource-constrained IIoT devices. Additionally, SecuFL-IoT ensures strong privacy guarantees ( \(\epsilon=0.9\) ) and improves adversarial robustness, reducing data poisoning success rates below 9%. However, the framework introduces encryption latency and assumes a static network topology, which may affect real-time adaptability in highly dynamic environments. In conclusion, SecuFL-IoT provides a scalable, privacy-preserving, and industry-compliant federated learning solution that aligns with ISA/IEC 62,443 cybersecurity standards, ensuring secure anomaly detection in smart factories, power grids, and other critical IIoT infrastructures.