Towards Securing IIoT: An Innovative Privacy-Preserving Anomaly Detector Based on Federated Learning

Computer Science > Cryptography and Security [Submitted on 7 Apr 2026] Towards Securing IIoT: An Innovative Privacy-Preserving Anomaly Detector Based on Federated Learning Samira Kamali Poorazad, Chafika Benzaïd, Tarik Taleb In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly detection system based on a novel Federated Learning (FL) framework. This system detects anomalies such as cyberattacks and protects industrial data privacy by processing data locally and training anomaly detection models on industrial agents without sharing raw data. The proposed FL framework incorporates two key components to enhance both privacy and efficiency. The first component is Homomorphic Encryption (HE), which is integrated into the framework to further protect sensitive data transmissions such as model parameters. HE enhances privacy in FL by preventing adversaries from inferring private industrial data through attacks, such as model inversion attacks. The second component is an innovative dynamic agent selection scheme, wherein a selection threshold is calculated based on agent delays and data size. The purpose of this new scheme is to mitigate the straggler effect and the communication bottleneck that occur in traditional FL architectures, such as synchronous and asynchronous architectures. It ensures that agents are not unfairly selected by the different delays resulting from heterogeneous data in IIoT environments, while simultaneously improving model performance and convergence speed. The proposed framework exhibits superior performance over baseline approaches in terms of accuracy, precision, F1-scores, communication costs, convergence speeds, and fairness rate. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.06101 [cs.CR]   (or arXiv:2604.06101v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.06101 Focus to learn more Submission history From: Samira Kamali Poorazad [view email] [v1] Tue, 7 Apr 2026 17:14:10 UTC (3,075 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)