Federated Learning-Based Distributed Localization of False Data Injection Attacks on Smart Grids

Data analysis and monitoring on smart grids are jeopardized by attacks on cyber-physical systems. False data injection attack (FDIA) is one of the attack classes that targets smart measurement devices by injecting malicious data. Machine learning techniques are proven to be effective in detecting and localizing FDIAs. Training of such models requires centralized processing of sensitive user data that may not be plausible in a practical scenario. By employing federated learning, it is possible to train a model for the detection and localization of FDIAs while preserving the privacy of sensitive user data. However, federated learning introduces new problems, such as the personalization of the detectors in each node. In this article, we propose a federated learning-based scheme combined with a hybrid deep neural network architecture that exploits the local correlations between the connected power buses by employing graph neural networks as well as the temporal patterns in the data by using long short term memory layers. The proposed mechanism offers flexible and efficient training of an FDIA detector in a distributed setup while preserving the privacy of the clients. We validate the proposed architecture by extensive simulations on the IEEE 57, 118, and 300 bus systems and real electricity load data.