Most of federated learning (FL) researches mainly focus on image and voice data at the expense of graph data. However, Graph Federated Learning (GFL) is specialized for FL on graphs, and received little attention. Subgraph FL is a branch of GFL. In the Subgraph FL situation, a graph is not stored centrally but is distributed among clients as multiple subgraphs. Each client owns a subgraph of the original graph and faces a unique challenge, i.e., missing information across clients. Ignoring the missing information across subgraphs will result in deterioration of the performance of the local model. In this paper, we consider data heterogeneity and bring up a more practical problem, i.e., the subgraphs on the clients are from multiple Non-IID graphs rather than the same global graph. Then, to address the issues, we propose a subgraph FL framework FedSG which can learn a personalized model for each client, benefiting from its ability to effectively separate and combine topology information and feature information among the subgraphs. Finally, our experimental results show that FedSG achieves higher accuracy performance and faster convergence, while significantly reducing communication cost, compared with the existing approaches.