FASTGNN: A Topological Information Protected Federated Learning Approach For Traffic Speed Forecasting
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Industrial Informatics, 2021, PP, (99), pp. 1-1
- Issue Date:
- 2021-01-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| FASTGNN.pdf | Accepted version | 6.35 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
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 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 architectures can only protect the data privacy; however, ignore the one of topological information of transportation networks. In this work, we propose a novel federated learning architecture 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 architecture 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.
Please use this identifier to cite or link to this item: