Intrusion Detection in Industrial IoT Gateway Networks A Comparative Study of Centralized Machine Learning and Federated Learning

Author(s)	Ayushi Mishra, Anav Sobti, Kinshi Sinha
Country	India
Abstract	This paper presents a comparative study of centralized machine learning and federated learning for intrusion detection in Industrial Internet of Things (IIoT) gateway networks using the X-IIoTID dataset. Centralized models achieve high detection accuracy but rely on data aggregation, raising privacy and scalability concerns. To address this, a federated learning framework based on the FedAvg algorithm is implemented with distributed clients. Experimental results show that centralized approaches achieve up to 99.85% accuracy, while federated learning achieves 98.38% with improved privacy, reduced communication overhead, and better scalability. The study demonstrates that federated learning provides a practical and privacy-preserving alternative for intrusion detection in real-world IIoT environments.
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-29
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.76530

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