Integrating Federated Learning and Differential Privacy for Secure Anomaly Detection in Smart Grids

Anomaly detection is essential for ensuring the safe and efficient operation of industrial systems like smart grids. Smart grid stations handle sensitive data and are often hesitant to share it with third-party servers for centralized anomaly detection. Federated Learning (FL) offers a viable solution to this issue by enhancing anomaly detection in smart grids without compromising data privacy. We present a method for developing an unsupervised anomaly detection system using FL applied to a synthetic dataset that mimics a real-world smart grid system's behavior. We focus on utilizing FL's long short-term memory autoencoder in short, LSTM-AE for anomaly detection. However, there are concerns about potential privacy breaches in the FL system. Hence, to address this issue, we propose to integrate differential privacy (DP) with FL for anomaly detection by adding artificial noise to parameters at the client side before aggregation. This method ensures data privacy while maintaining the convergence of federated learning algorithms. Moreover, this research determines the optimal privacy level to balance noise scale and model accuracy. Our findings suggest a criterion for selecting the right privacy budget of DP based on the requirement of the system to provide good level of privacy in the system while maintaining the f1-score of FL-based anomaly detection system greater than 90%.