Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems

Zhang Yiming, Xie Fang (), Olena Hordiichuk-Bublivska, Halyna Beshley and Mykola Beshley ()
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Zhang Yiming: Detroit Green Technology Institute, Hubei University of Technology, Wuhan 430068, China
Xie Fang: School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Olena Hordiichuk-Bublivska: Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, 79013 Lviv, Ukraine
Halyna Beshley: Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, 79013 Lviv, Ukraine
Mykola Beshley: Department of Telecommunications, Lviv Polytechnic National University, Bandera Str. 12, 79013 Lviv, Ukraine

Energies, 2023, vol. 16, issue 16, 1-15

Abstract: The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems.

Keywords: IIoT; big data; distributed systems; machine learning; anomaly detection; SVD; FedSVD (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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