Paper ID: 2111.06750

STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks

Guannan Lou, Yuze Liu, Tiehua Zhang, Xi Zheng

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.

Submitted: Nov 12, 2021