This study proposes a federated learning algorithm for transient stability in a distributed power system and a Byzantine node detection algorithm to assess the transient stability of various regions in a distributed smart grid and address potential network attacks. In the federated learning framework, each regional power grid independently uses neural networks to assess its transient stability, while the central server integrates the training gradients, provides feedback, and updates them. To improve the security of the framework, the model constructed in this study clusters the updated gradients of each regional power grid to identify outliers, which refer to regional power grids that are under attack, so as to detect Byzantine nodes. Considering the high-dimensional characteristics of gradients, direct clustering will lead to inaccurate distance measurement. Therefore, an autoencoder is trained online to reduce the dimension of the gradients. Density clustering is then performed on the lower-dimensional gradients to select a small number of nodes as a set of Byzantine nodes and permanently eliminate the gradients provided by Byzantine nodes. An example of electromechanical transient simulation for angle stability is used for verification. The results show that this method addresses network attacks while assessing the temporary stability of the power system. Compared with other methods, this method significantly improves the average accuracy and stability, effectively preventing fluctuations in assessment accuracy.