Efficient Energy Theft Detection Utilizing Hierarchical Federated Learning

Energy theft in smart grids is a widespread challenge that impacts utility providers and consumers. It undermines the financial stability of utility companies, introduces electrical safety risks, and contributes to increased energy costs. The advancement of machine learning (ML) combined with the amount of data produced by smart grids can help resolve the problem by building efficient detection ML models. Data privacy is a hugely controversial issue in this case. Federated learning (FL) can be used to rescue as it enables the training of ML models while preserving data privacy by keeping data localized. However, it suffers compatibility issues, high communication costs, security threats, and scalability challenges. To address these limitations, this paper proposes to use hierarchical federated learning (HFL) for energy theft detection, where intermediate aggregations occur at edge servers, resolving compatibility issues, significantly reducing communication costs and enhancing scalability. It demonstrates how the novel HFL approach can efficiently manage and process data from distributed sources. Our experimental results demonstrate an 88% reduction in communication overhead and high scalability compared to FL. In terms of accuracy and computational efficiency, our approach is comparable to both FL and centralized ML.