  We present a spatial-temporal federated learning framework for graph neural
networks, namely STFL. The framework explores the underlying correlation of the
input spatial-temporal data and transform it to both node features and
adjacency matrix. The federated learning setting in the framework ensures data
privacy while achieving a good model generalization. Experiments results on the
sleep stage dataset, ISRUC_S3, illustrate the effectiveness of STFL on graph
prediction tasks.
