FedDP: A Privacy-Protecting Theft Detection Scheme in Smart Grids Using Federated Learning

Muhammad Mansoor Ashraf, Muhammad Waqas, Ghulam Abbas, Thar Baker (), Ziaul Haq Abbas and Hisham Alasmary
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Muhammad Mansoor Ashraf: Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Swabi 23460, Pakistan
Muhammad Waqas: Computer Engineering Department, College of Information Technology, University of Bahrain, Shakir 32038, Bahrain
Ghulam Abbas: Faculty of Computer Science and Engineering, GIK Institute of Engineering Sciences and Technology, Swabi 23460, Pakistan
Thar Baker: Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates
Ziaul Haq Abbas: Faculty of Electrical Engineering, GIK Institute of Engineering Sciences and Technology, Swabi 23460, Pakistan
Hisham Alasmary: Department of Computer Science, College of Computer Science, King Khalid University, Abha 62529, Saudi Arabia

Energies, 2022, vol. 15, issue 17, 1-15

Abstract: In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data.

Keywords: federated learning; smart grids; federated voting classifier; privacy protection; theft detection (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: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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