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STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks. (arXiv:2111.06750v2 [cs.LG] UPDATED)
Jan. 12, 2022, 2:11 a.m. | Guannan Lou, Yuze Liu, Tiehua Zhang, Xi Zheng
cs.LG updates on arXiv.org arxiv.org
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.
arxiv federated learning framework graph graph neural networks learning networks neural networks
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