Federated Learning Based Distributed Localization of False Data
  Injection Attacks on Smart Grids
Cihat Keçeci Katherine R. Davis Erchin Serpedin
Data analysis and monitoring on smart grids are jeopardized by attacks on
cyber-physical systems. False data injection attack (FDIA) is one of the
classes of those attacks that target the smart measurement devices by injecting
malicious data. The employment of machine learning techniques in the detection
and localization of FDIA is proven to provide effective results. Training of
such models requires centralized processing of sensitive user data that may not
be plausible in a practical scenario. By employing federated learning for the
detection of FDIA attacks, it is possible to train a model for the detection
and localization of the attacks 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 paper, 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 LSTM 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.