A masking-based federated singular value decomposition method for anomaly detection in industrial internet of things

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.