  Recently, Graph Neural Network (GNN) has achieved remarkable success in
various real-world problems on graph data. However in most industries, data
exists in the form of isolated islands and the data privacy and security is
also an important issue. In this paper, we propose FedVGCN, a federated GCN
learning paradigm for privacy-preserving node classification task under data
vertically partitioned setting, which can be generalized to existing GCN
models. Specifically, we split the computation graph data into two parts. For
each iteration of the training process, the two parties transfer intermediate
results to each other under homomorphic encryption. We conduct experiments on
benchmark data and the results demonstrate the effectiveness of FedVGCN in the
case of GraphSage.
