The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
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 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]
Citations
Cites
Graph Attention NetworksPreprint
Veličković, P., Casanova, A., Liò, P., Cucurull, G., Romero, A., & Bengio, Y. (2017) · arXiv (Cornell University) · doi:10.17863/cam.48429 · Cited by 19,745
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1710.10903 · Cited by 12,107
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Yu, B., Yin, H., & Zhu, Z. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/505 · Cited by 6,054
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2014) · Foundations and Trends® in Theoretical Computer Science · doi:10.1561/0400000042 · Cited by 5,557
The Algorithmic Foundations of Differential Privacy
Dwork, C., & Roth, A. (2013) · doi:10.1561/9781601988195 · Cited by 3,856
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Wu, Z., Pan, S., Long, G., Jiang, J. B., & Chengqi Zhang, B. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/264 · Cited by 3,713
Self-citationDiffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingPreprint
Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01926 · Cited by 2,597
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
Wei, K., Li, J., Ding, M., Ma, C., Yang, H. H., Farokhi, F., Jin, S., Quek, T. Q. S., & Vincent Poor, H. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.2988575 · Cited by 2,400
Research works printed but not linked: 1
- cui, 2016, Deep stacked bidirectional and unidirectional lstm recurrent neural network for network-wide traffic speed prediction, 6th International Workshop on Urban Computing (UrbComp 2017)
Cited by
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Federated learning for smart cities: A comprehensive survey
Pandya, S., Srivastava, G., Jhaveri, R. H., Babu, M. R., Bhattacharya, S., Maddikunta, P. K. R., Mastorakis, S., Piran, M. J., & Gadekallu, T. R. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102987 · Cited by 317
A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions
Aouedi, O., Vu, T.-H., Sacco, A., Nguyen, D. C., Piamrat, K., Marchetto, G., & Pham, Q. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3430368 · Cited by 345
Shaygan, M., Meese, C., Li, W., Zhao, X., & Nejad, M. (2022) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2022.103921 · Cited by 307
Deep learning for time series forecasting: a survey
Kong, X., Chen, Z., Liu, W., Ning, K., Zhang, L., Marier, S. M., Liu, Y., Chen, Y., & Xia, F. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-025-02560-w · Cited by 315
Federated Graph Neural Networks: Overview, Techniques, and Challenges
Liu, R., Xing, P., Deng, Z., Li, A., Guan, C., & Yu, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3360429 · Cited by 240
Metadata
- 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
- Social Sciences › Transportation › 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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