Privacy-preserving and Efficient Decentralized Federated Learning-based Energy Theft Detector

Energy theft causes economic losses and power out-ages and disrupts energy generation and distribution of smart grids. A significant challenge is how to effectively use customers' power consumption data for energy theft detection while pre-serving security and privacy. One solution is to use federated learning (FL) to compute a global model to detect energy theft cyberattacks where detection stations train local models on their customers' power consumption data and send only the parameters of the models to an aggregator server. Nevertheless, revealing the model's parameters may still leak customers' private data by launching attacks such as membership and inference. Therefore, a secure aggregation scheme is needed to protect the models' param-eters. Furthermore, the existing privacy-preserving aggregation schemes suffer from high overhead and low model accuracy. This paper addresses these limitations by proposing a novel privacy- preserving, efficient, decentralized, aggregation scheme based on a functional encryption cryptosystem for energy theft detection in smart grids without requiring a key distribution center. Our scheme enables the detection stations to send encrypted training parameters to an aggregator, which calculates the aggregated parameters and returns the updated model parameters to the detection stations without being able to learn the parameters of the local models or the training data of the customers to preserve their privacy. Moreover, the results of our extensive experiments show that our FL-based detector can detect energy thefts accurately with low overhead because of our lightweight privacy-preserving aggregation scheme.