  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 vision and language domains, there is no suitable
platform for the federated training of GNNs. To this end, we introduce
FedGraphNN, an open research federated learning system and a 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 contribute a new molecular dataset,
hERG, to promote research exploration. Our experimental results present
significant challenges in federated GNN training: federated GNNs perform worse
in most datasets with a non-I.I.D split than centralized GNNs; the GNN model
that attains the best result in the centralized setting may not hold its
advantage in the federated setting. These results imply that more research
efforts are needed to unravel the mystery behind federated GNN training.
Moreover, our system performance analysis demonstrates that the FedGraphNN
system is computationally affordable to most research labs with limited GPUs.
We maintain the source code at https://github.com/FedML-AI/FedGraphNN.
