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FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting.

Authors :
Zhang, Chenhan
Zhang, Shuyu
Q. Yu, James J.
Yu, Shui
Source :
IEEE Transactions on Industrial Informatics; Dec2021, Vol. 17 Issue 12, p8464-8474, 11p
Publication Year :
2021

Abstract

Federated learning has been applied to various tasks in intelligent transportation systems to protect data privacy through decentralized training schemes. The majority of the state-of-the-art models in intelligent transportation systems (ITS) are graph neural networks (GNN)-based for spatial information learning. When applying federated learning to the ITS tasks with GNN-based models, the existing frameworks can only protect the data privacy; however, ignore the one of topological information of transportation networks. In this article, we propose a novel federated learning framework to tackle this problem. Specifically, we introduce a differential privacy-based adjacency matrix preserving approach for protecting the topological information. We also propose an adjacency matrix aggregation approach to allow local GNN-based models to access the global network for a better training effect. Furthermore, we propose a GNN-based model named attention-based spatial-temporal graph neural networks (ASTGNN) for traffic speed forecasting. We integrate the proposed federated learning framework and ASTGNN as FASTGNN for traffic speed forecasting. Extensive case studies on a real-world dataset demonstrate that FASTGNN can develop accurate forecasting under the privacy preservation constraint. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15513203
Volume :
17
Issue :
12
Database :
Complementary Index
Journal :
IEEE Transactions on Industrial Informatics
Publication Type :
Academic Journal
Accession number :
153244566
Full Text :
https://doi.org/10.1109/TII.2021.3055283