  Graph data are ubiquitous in the real world. Graph learning (GL) tries to
mine and analyze graph data so that valuable information can be discovered.
Existing GL methods are designed for centralized scenarios. However, in
practical scenarios, graph data are usually distributed in different
organizations, i.e., the curse of isolated data islands. To address this
problem, we incorporate federated learning into GL and propose a general
Federated Graph Learning framework FedGL, which is capable of obtaining a
high-quality global graph model while protecting data privacy by discovering
the global self-supervision information during the federated training.
Concretely, we propose to upload the prediction results and node embeddings to
the server for discovering the global pseudo label and global pseudo graph,
which are distributed to each client to enrich the training labels and
complement the graph structure respectively, thereby improving the quality of
each local model. Moreover, the global self-supervision enables the information
of each client to flow and share in a privacy-preserving manner, thus
alleviating the heterogeneity and utilizing the complementarity of graph data
among different clients. Finally, experimental results show that FedGL
significantly outperforms baselines on four widely used graph datasets.
