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

Autor:	Lou, Guannan, Liu, Yuze, Zhang, Tiehua, Zheng, Xi
Rok vydání:	2021
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2111.06750 Zobrazit plný text záznamu View this record from Arxiv