Cited by 195Traffic Prediction and Management TechniquesPrivacy-Preserving Technologies in DataTraffic control and management

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

Zhang, Chenhan, Zhang, Shuyu, Yu, James J. Q., Yu, Shui·2021·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2021.3055283

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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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Authors: Zhang, C., Zhang, S., Yu, J. J. Q., & Yu, S.
Affiliations: University of Technology Sydney (AU)
Southern University of Science and Technology (CN)
ORCID: Zhang, Chenhan
Zhang, Shuyu
Yu, James J. Q.
Yu, Shui
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-12
Volume: 17
Issue: 12
Pages: 8464-8474
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2021.3055283
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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FASTGNN: A Topological Information Protected Federated Learning Approach for Traffic Speed Forecasting

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