FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting
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.
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FASTGNN can provide similar performance compared with three baseline algorithms, where MAPE of FASTGNN is only 0.31% more than MAPE of STGCN.
“FASTGNN can provide similar performance compared with the three baseline algorithms, where the MAPE of FASTGNN is only 0.31% more than MAPE of STGCN.”
From Applications of Federated Learning; Taxonomy, Challenges, and Research Trends (2022) · cites this paper as [37]
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- Authors
- Zhang, C., Zhang, S., Yu, J. J. Q., & Yu, S.
- Affiliations
- University of Technology Sydney (AU)
- Southern University of Science and Technology (CN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-12
- Volume
- 17
- Issue
- 12
- Pages
- 8464-8474
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3126441351
- Cited by
- 194
- References
- 38
- Citations by year
- 2026: 22 · 2025: 45 · 2024: 55 · 2023: 37 · 2022: 26 · 2021: 9
- FWCI
- 14.56
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- General Program of Guangdong Basic and Applied Basic Research Foundation (2019A1515011032)
- Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation (2020B121201001)
- Australian (ARC DP200101374)
- Topics
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Keywords
- federated learning, graph neural networks, traffic speed prediction, differential privacy, topological information, intelligent transportation systems, spatio-temporal graph neural networks, attention mechanism, data privacy, transportation networks
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