Privacy-preserving energy theft detection based on federated learning

This paper proposes a secure and privacy-preserving framework for detecting unauthorized energy usage by leveraging consumer energy consumption data in smart grids. Addressing the limitations of traditional centralized schemes, a secure privacy-preserving federated learning framework, named Paillier-Encrypted Federated Learning-based Detection (PE-FLD), is adopted. This architecture consists of a global centre and multiple local detection centers, which interact solely with local consumer data and subsequently communicate aggregated parameters to the global center, thereby safeguarding user privacy. Furthermore, a modified Transformer Neural Network is employed for energy theft monitoring in smart meters. Experimental validation is conducted using real energy consumption data from the Irish Electricity Metering Dataset.