  Graph Neural Network (GNN) research is rapidly growing thanks to the capacity
of GNNs in learning distributed representations from graph-structured data.
However, centralizing a massive amount of real-world graph data for GNN
training is prohibitive due to privacy concerns, regulation restrictions, and
commercial competitions. Federated learning (FL), a trending distributed
learning paradigm, provides possibilities to solve this challenge while
preserving data privacy. Despite recent advances in vision and language
domains, there is no suitable platform for the FL of GNNs. To this end, we
introduce FedGraphNN, an open FL benchmark system that can facilitate research
on federated GNNs. FedGraphNN is built on a unified formulation of graph FL and
contains a wide range of datasets from different domains, popular GNN models,
and FL algorithms, with secure and efficient system support. Particularly for
the datasets, we collect, preprocess, and partition 36 datasets from 7 domains,
including both publicly available ones and specifically obtained ones such as
hERG and Tencent. Our empirical analysis showcases the utility of our benchmark
system, while exposing significant challenges in graph FL: federated GNNs
perform worse in most datasets with a non-IID split than centralized GNNs; the
GNN model that attains the best result in the centralized setting may not
maintain its advantage in the FL setting. These results imply that more
research efforts are needed to unravel the mystery behind federated GNNs.
Moreover, our system performance analysis demonstrates that the FedGraphNN
system is computationally efficient and secure to large-scale graphs datasets.
We maintain the source code at https://github.com/FedML-AI/FedGraphNN.
