  As an important cyber-physical system (CPS), smart grid is highly vulnerable
to cyber attacks. Amongst various types of attacks, false data injection attack
(FDIA) proves to be one of the top-priority cyber-related issues and has
received increasing attention in recent years. However, so far little attention
has been paid to privacy preservation issues in the detection of FDIAs in smart
grid. Inspired by federated learning, a FDIA detection method based on secure
federated deep learning is proposed in this paper by combining Transformer,
federated learning and Paillier cryptosystem. The Transformer, as a detector
deployed in edge nodes, delves deep into the connection between individual
electrical quantities by using its multi-head self-attention mechanism. By
using federated learning framework, our approach utilizes the data from all
nodes to collaboratively train a detection model while preserving data privacy
by keeping the data locally during training. To improve the security of
federated learning, a secure federated learning scheme is designed by combing
Paillier cryptosystem with federated learning. Through extensive experiments on
the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of
the proposed method are verifed.
