  Federated learning has attracted much research attention due to its privacy
protection in distributed machine learning. However, existing work of federated
learning mainly focuses on Convolutional Neural Network (CNN), which cannot
efficiently handle graph data that are popular in many applications. Graph
Convolutional Network (GCN) has been proposed as one of the most promising
techniques for graph learning, but its federated setting has been seldom
explored. In this paper, we propose FedGraph for federated graph learning among
multiple computing clients, each of which holds a subgraph. FedGraph provides
strong graph learning capability across clients by addressing two unique
challenges. First, traditional GCN training needs feature data sharing among
clients, leading to risk of privacy leakage. FedGraph solves this issue using a
novel cross-client convolution operation. The second challenge is high GCN
training overhead incurred by large graph size. We propose an intelligent graph
sampling algorithm based on deep reinforcement learning, which can
automatically converge to the optimal sampling policies that balance training
speed and accuracy. We implement FedGraph based on PyTorch and deploy it on a
testbed for performance evaluation. The experimental results of four popular
datasets demonstrate that FedGraph significantly outperforms existing work by
enabling faster convergence to higher accuracy.
