  Currently, graph learning models are indispensable tools to help researchers
explore graph-structured data. In academia, using sufficient training data to
optimize a graph model on a single device is a typical approach for training a
capable graph learning model. Due to privacy concerns, however, it is
infeasible to do so in real-world scenarios. Federated learning provides a
practical means of addressing this limitation by introducing various
privacy-preserving mechanisms, such as differential privacy (DP) on the graph
edges. However, although DP in federated graph learning can ensure the security
of sensitive information represented in graphs, it usually causes the
performance of graph learning models to degrade. In this paper, we investigate
how DP can be implemented on graph edges and observe a performance decrease in
our experiments. In addition, we note that DP on graph edges introduces noise
that perturbs graph proximity, which is one of the graph augmentations in graph
contrastive learning. Inspired by this, we propose leveraging graph contrastive
learning to alleviate the performance drop resulting from DP. Extensive
experiments conducted with four representative graph models on five widely used
benchmark datasets show that contrastive learning indeed alleviates the models'
DP-induced performance drops.
