Graph Neural Network (GNN) research is rapidly growing thanks to the capacity of GNNs to learn representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns, regulation restrictions, and commercial competition. Federated learning (FL), a trending distributed learning paradigm, aims to solve this challenge while preserving privacy. Despite recent advances in CV and NLP, there is no suitable platform for the federated training of GNNs. To this end, we introduce FedGraphNN, an open research federated learning system and the benchmark to facilitate GNN-based FL research. FedGraphNN is built on a unified formulation of federated GNNs and supports commonly used datasets, GNN models, FL algorithms, and flexible APIs. We also include a new molecular dataset, hERG, to promote research exploration. Our experimental results present significant challenges from federated GNN training: federated GNNs perform worse in most datasets with a non-I.I.D split than centralized GNNs; the GNN model that performs the best in centralized training may not hold its advantage in the federated setting. These results imply that more research effort is needed to unravel the mystery of federated GNN training. Moreover, our system performance analysis demonstrates that the FedGraphNN system is affordable to most research labs with a few GPUs. FedGraphNN will be regularly updated and welcomes inputs from the community.