Abstract: A satellite fault diagnosis approach based on federated learning is proposed to address issues such as single satellite under configuration and incomplete measurement information. Firstly, a fault model for satellites is established, and fault data is generated via the unity simulation environment. Then, a Bidirectional Coordination Network (BicNet) is used to construct local training models, which considers neighboring satellite fault information for decision making. The diagnostic network does not need to be retrained when the number of formation satellites changes, enabling plug and play. Finally, a federated learning framework is used for distributed training, integrating fault features of the entire satellite group without increasing communication pressure. Each satellite uploads local model parameters for collaborative modeling, improving the fault diagnosis capability for different fault types of satellite group and completing the fault diagnosis. Simulation results demonstrate high accuracy of 99% on the test set, indicating the effectivenessw of proposed method.

Key words: federated learning, fault diagnosis, satellite formation, reinforcement learning