Research Area: Machine Learning
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.
Keywords:
Author(s) Name: Chenhan Zhang; Shuyu Zhang; James J. Q. Yu; Shui Yu
Journal name: IEEE Transactions on Industrial Informatics
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TII.2021.3055283
Volume Information: ( Volume: 17, Issue: 12, Dec. 2021) Page(s): 8464 - 8474
