The rapid development of smart grids has led to more efficient power transmission, and the high level of integration of grid systems and ICTs has exposed power systems to more cyber threats. Intrusion detection has received a lot of attention as an effective method to detect cyber attacks, and most of the existing schemes are based on the strong assumption that a single organization has enough high-quality attack examples and is willing to share their data. However, in real life, individual institutions not only generate a small amount of data but also have individual characteristics and are usually not willing to share their data, and using such single institution data is not sufficient to train a general model with high accuracy. In view of this, this paper proposed a secure and efficient approach for smart grid intrusion detection. Specifically, first, a federated learning framework was introduced to collaboratively train a generic intrusion detection model to protect the security of local data and allow indirect expansion of the data volume; Second, a secure communication protocol was designed to protect the security of model parameters in training and prevent eavesdroppers from eavesdropping on them for inference attacks; Finally, by selecting a good client for global aggregation, the fast convergence of the model was guaranteed and the number of participants was reduced to reduce the communication bandwidth. The experimental results show that the accuracy of intrusion detection is improved, data privacy is protected, and communication cost is reduced while ensuring model convergence.
