Fake reviews are spread all over the network platform, which is very harmful to users, businesses and the platform itself. In recent years, graph neural networks have been widely used in fraud detection problems. The graph neural network aggregates the neighborhood information of nodes through different relationships to reveal the suspiciousness of nodes. However, previous work has been based on a single client training model, without integrating data information from multiple clients for federated training, resulting in overall poor performance. Therefore, we propose a new framework called FGFD (Federated Graph Fraud Detection) to learn the characteristics of fake reviews on different platforms while protecting data privacy through federated learning, and improving overall performance. Specifically, we first design a GNN model, called GFD, which deployed the model on all clients in the system, and then FGFD through many clients (such as different e-commerce platforms) under the coordination of a central service provider server to train GFD, GFD training carried out under the local client only, and the model parameters are exchanged between the client and the central server without exchanging data, thereby realizing the protection of client data privacy. The performance of GFD will learns the characteristics of fake reviews in different client in the process of exchanging data, thereby improving the overall performance. We verified the performance of the model on the Amazon and YELP datasets, the FGFD with federated learning graph neural network models outperformed other graph neural network models.