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

2021-11-12Code Available0· sign in to hype

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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