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arXiv · 2111.06750

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

Guannan Lou·Yuze Liu·Tiehua Zhang·Xi Zheng
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Abstract

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.

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Guannan Lou, Yuze Liu, Tiehua Zhang, Xi Zheng. 2021-11-12. STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks. https://arxiv.org/abs/2111.06750

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