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

Guannan Lou; Yuze Liu; Tiehua Zhang; Xi Zheng

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

Machine Learning 2022-01-12 v2 Artificial Intelligence

Authors: Guannan Lou , Yuze Liu , Tiehua Zhang , Xi Zheng

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.

Keywords

spatio-temporal learning federated graph learning federated learning

Cite

@article{arxiv.2111.06750,
  title  = {STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks},
  author = {Guannan Lou and Yuze Liu and Tiehua Zhang and Xi Zheng},
  journal= {arXiv preprint arXiv:2111.06750},
  year   = {2022}
}

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