Title: A masking-based federated singular value decomposition method for anomaly detection in industrial internet of things
Authors: Olena Hordiichuk-Bublivska; Halyna Beshley; Natalia Kryvinska; Mykola Beshley
Addresses: Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, Lviv 79013, Ukraine ' Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, Lviv 79013, Ukraine; Department of Information Systems, Faculty of Management, Comenius University in Bratislava, 82005 Bratislava 25, Slovakia ' Department of Information Systems, Faculty of Management, Comenius University in Bratislava, 82005 Bratislava 25, Slovakia ' Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, Lviv 79013, Ukraine; Department of Information Systems, Faculty of Management, Comenius University in Bratislava, 82005 Bratislava 25, Slovakia
Abstract: The industrial internet of things (IIoT) is a flexible and scalable manufacturing system that can collect and analyse data from sensors based on machine learning, cloud, and edge computing. Recommendation systems can identify patterns in big data and reduce irrelevant data, with the singular value decomposition (SVD) algorithm being commonly used. Based on the found regularities in the data, it is possible to predict the most probable future events, such as emergency shutdowns of equipment, the occurrence of emergencies, etc. This paper explores the SVD method for anomaly detection in IIoT and proposes the federated singular value decomposition (FedSVD) method, which better protects large-scale IIoT data privacy. Results show FedSVD has greater accuracy and duration of calculations. A masking-based FedSVD method is proposed for anomaly detection and data protection. Choosing the optimal algorithm for IIoT and recommendation systems can automate the processing of critical parameters and improve efficiency.
Keywords: industrial internet of things; IIoT; big data; distributed systems; machine learning; recommendation systems; singular value decomposition; SVD; federated singular value decomposition; FedSVD; edge computing; cloud computing.
DOI: 10.1504/IJWGS.2023.133502
International Journal of Web and Grid Services, 2023 Vol.19 No.3, pp.287 - 317
Received: 04 Apr 2022
Received in revised form: 31 Mar 2023
Accepted: 17 Apr 2023
Published online: 18 Sep 2023 *