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Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach
Open accessAbstract
Existing traffic flow forecasting approaches by deep learning models achieve excellent success based on a large volume of data sets gathered by governments and organizations. However, these data sets may contain lots of user's private data, which is challenging the current prediction approaches as user privacy is calling for the public concern in recent years. Therefore, how to develop accurate traffic prediction while preserving privacy is a significant problem to be solved, and there is a tradeoff between these two objectives. To address this challenge, we introduce a privacy-preserving machine learning technique named federated learning (FL) and propose an FL-based gated recurrent unit neural network algorithm (FedGRU) for traffic flow prediction (TFP). FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism rather than directly sharing raw data among organizations. In the secure parameter aggregation mechanism, we adopt a federated averaging algorithm to reduce the communication overhead during the model parameter transmission process. Furthermore, we design a joint announcement protocol to improve the scalability of FedGRU. We also propose an ensemble clustering-based scheme for TFP by grouping the organizations into clusters before applying the FedGRU algorithm. Extensive case studies on a real-world data set demonstrate that FedGRU can produce predictions that are merely 0.76 km/h worse than the state of the art in terms of mean average error under the privacy preservation constraint, confirming that the proposed model develops accurate traffic predictions without compromising the data privacy.
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“In [85], an improved FedAvg that uses random subsampling of participants has helped reduce communication costs by limiting the number of participants in each round.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [85]
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“Medicine resistance or therapy against various diseases, as well as survival rates or explanations, can be examined with strong privacy towards sensitive data [165].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [165]
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Semi-supervised classification with graph convolutional networks
Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853
Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387
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
Self-citationFederated learning: Challenges, methods, and future directions
Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020) · IEEE Signal Processing Magazine · doi:10.1109/msp.2020.2975749 · Cited by 6,210
Federated Optimization in Heterogeneous Networks
Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. (2018) · arXiv · doi:10.48550/arxiv.1812.06127 · Cited by 4,023
Federated Learning: Strategies for Improving Communication Efficiency
Konečný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., & Bacon, D. (2016) · arXiv · doi:10.48550/arxiv.1610.05492 · Cited by 3,766
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317
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Items 501–600 of 713
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Jian, W., He, J., Chen, J., & Cai, M. (2023) · Journal of Advanced Transportation · doi:10.1155/2023/8333560 · Cited by 2
Salih, K. M. M., & Ibraheem, N. B. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/150198 · Cited by 2
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Ma, T. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/49/20241157 · Cited by 2
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Wang, S., Xia, W., Zhao, H., Ni, Y., Zhu, C., & Zhu, H. (2024) · IEEE Wireless Communications and Networking Conference · doi:10.1109/wcnc57260.2024.10570575 · Cited by 2
Clustering-Based Federated Learning for Enhancing Data Privacy in Internet of Vehicles
Jin, Z., Wang, J., & Zhang, L. (2024) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2024.06.003 · Cited by 2
Multi-Unmanned Aerial Vehicle Agricultural Inspection System Based on Federated Learning
Wu, Z., & Lin, S. (2024) · International Conference on Computer and Communication Systems (ICCCS) · doi:10.1109/icccs61882.2024.10602809 · Cited by 4
Network-aware federated neural architecture search
Öcal, G., & Özgövde, A. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.053 · Cited by 2
Lin, Y., Lu, X., Wang, Y., Jiang, Y., & Mao, W. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-7235-3_29 · Cited by 2
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Veiga, R., Sousa, J., Morais, R., Bastos, L., Junior, W. V. L., do Rosário, D. L., & Cerqueira, E. (2024) · Journal of the Brazilian Computer Society · doi:10.5753/jbcs.2024.4325 · Cited by 2
A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
Marcillo, P., Suntaxi, G., & Hernández-Álvarez, M. (2024) · Applied Sciences · doi:10.3390/app14219876 · Cited by 2
Xing, T., & Zhang, M. (2024) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit62299.2024.10827931 · Cited by 2
Traffic Forecasting Using Federated Randomized High-Order Fuzzy Cognitive Maps
Orang, O., Silva, F., Silva, P. C. D. L. E., Barros, P. H., Ramos, H. S., & Guimarães, F. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-79032-4_31 · Cited by 2
A clustering-based approach for classifying data streams using graph matching
Du, Y., He, M., & Wang, X. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01087-9 · Cited by 2
Privacy-Preserving Short-Term Travel Demand Forecasting Based on Federated Learning
Du, R., He, D., Song, Y., Ding, Z., Chan, S., & Li, X. (2025) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2025.3547823 · Cited by 3
Edge-assisted U-shaped split federated spatial–temporal attention GCN for traffic flow prediction
Ren, F., Liu, D., Liu, H., & Cao, Y. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107965 · Cited by 2
Privacy preserving hierarchical federated learning approach for secure urban mobility prediction
Naresh, V. S., & Ayaaappa, D. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02053-1 · Cited by 2
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Yang, L., Liu, Q., Zhao, Z., Wang, Y., & Gu, J. (2025) · Complex & Intelligent Systems · doi:10.1007/s40747-025-02007-3 · Cited by 4
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Lonare, S., & Bhramaramba, R. (2021) · Journal of Engineering Science and Technology Review · doi:10.25103/jestr.143.13 · Cited by 2
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Lin, G., Zhu, X., Wang, J., & Xiao, J. Z. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533419 · Cited by 2
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Suzuki, J., Lameh, S. F., & Amannejad, Y. (2021) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta53674.2021.9660819 · Cited by 3
Qi, Y., & Liu, G. (2023) · IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS) · doi:10.1109/icpics58376.2023.10235479 · Cited by 2
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Wang, S., Zhao, H., Xia, W., Wen, W., Wang, B., & Zhu, H. (2023) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct59356.2023.10419821 · Cited by 2
A Parallel Hybrid Deep Learning Model for Turning Movement Flow Prediction
Liu, C., Yu, S., Zhang, A., Xiao, Q., Song, D., & Pan, G. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10451420 · Cited by 2
Le, J., Xing, B., Zhang, D., & Qiao, D. (2024) · Mathematics · doi:10.3390/math12111722 · Cited by 3
Shuang, M., Zhang, Z., & Zhao, Y. (2023) · International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn60784.2023.00037 · Cited by 2
Short-term traffic flow prediction based on cuckoo search-wavelet neural networkPreprint
Lv, H., Kang, Y., & Shen, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-778000/v1 · Cited by 2
Short-time traffic flow prediction based on improved LSSVMPreprint
Ueda, K., Abe, S., & Shen, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-702558/v1 · Cited by 2
Federated Ensembles: a literature reviewPreprint
van Daalen, F., Ippel, L., Dekker, A. L. A. J., & Bermejo, I. (2022) · Research Square · doi:10.21203/rs.3.rs-2350540/v1 · Cited by 2
Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
“It also mentions how these techniques can be applied are applied in the context of ML-based IoV applications.”
Privacy-Preserving Traffic Violation Image Filtering and Searching via Crowdsensing
Zhang, Y., Xiong, J., & Liu, X. (2022) · Mobile Networks and Applications · doi:10.1007/s11036-021-01882-7 · Cited by 1
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Huang, Z., Zhou, M., Liu, S., & You, L. (2023) · International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) · doi:10.1109/mt-its56129.2023.10241670 · Cited by 1
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Liu, Q., Wang, Y., Zhao, W., & Qiu, X. (2023) · IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) · doi:10.1109/dasc/picom/cbdcom/cy59711.2023.10361337 · Cited by 1
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Lin, T.-Y., & Tsai, Y.-L. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444309 · Cited by 1
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Liu, J., Yin, Z., Nie, L., & Zhao, X. (2024) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2024-01-2873 · Cited by 1
Federated Learning Implementation with Privacy Leakage Prevention for Hand-Written Digit Recognition
Priyadarsini, N. I., & Raja, G. (2024) · International journal of electrical and computer engineering systems · doi:10.32985/ijeces.15.5.4 · Cited by 2
Vehicle Trajectory Generation Based on Generation Adversarial Network
He, Z., Shao, R., & Xiang, S. (2024) · Journal of Highway and Transportation Research and Development (English Edition) · doi:10.26599/htrd.2024.9480017 · Cited by 1
An End-Cloud Collaborative Federated Learning Debugging Framework for Data Heterogeneity
Kong, C., Meng, D., Fu, Z., Pei, R., Wu, J., Zhu, H., & Zhan, T. (2024) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126625501117 · Cited by 1
Advancements and Challenges in Federated Learning : A Survey
Prajapat, V., Rathore, N. P. S., Sethi, K. K., & Rajput, S. S. (2024) · IEEE International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET) · doi:10.1109/acroset62108.2024.10743243 · Cited by 1
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Zhang, X., Chen, T., Liang, H., Zeng, J., Wu, Y., & Zhang, F. (2024) · IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT) · doi:10.1109/iccasit62299.2024.10827979 · Cited by 1
A systematic review of the foundations, applications, and challenges of federated learning
Fateh, H., Rezvani, M., & Tahanian, E. (2024) · Archives de l'Institut d'Hessarek. · doi:10.61186/jsdp.21.3.23 · Cited by 1
Adaptive Timing Control of Parameter Aggregation in Vehicular Federated Learning
Ono, S., & Nakao, A. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10930106 · Cited by 1
Chen, X., Zhang, K., Chuai, G., Gao, W., Liu, X., Zhang, Y., & Hou, Y. (2025) · Digital Communications and Networks · doi:10.1016/j.dcan.2025.04.003 · Cited by 1
FedSTG: Breaking through Spatio-Temporal Data Silos with Federated Graph Learning
Zhang, Y., Wang, X., Yu, X., Yang, K., Zhou, Z., & Wang, Y. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3715562 · Cited by 1
CRATE: Privacy-Preserving Travel Time Estimation
Yu, J. J. Q. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3583004 · Cited by 1
Self-citationJoint AAV Location and Training Optimization for Air–Ground Integrated Online Federated Learning
Jing, Y., Qu, Y., Qin, Z. Q., Dong, C., Zhou, F., Wang, X., Guo, S., & Wu, Q. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3600241 · Cited by 1
Heterogeneous-Aware Traffic Prediction: A Privacy-Preserving Federated Learning Framework
Zeng, Z., Fang, Z., Huang, Y., Wang, Q., Chen, L., & Gao, Y. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00038 · Cited by 1
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Guo, K., Hu, X., Zhang, Z., Liu, C., & Zhang, Q. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06813-7 · Cited by 1
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Das, S., Hasib, A., Ashiqussalehin, M., & Fardous, J. (2025) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain66474.2025.11172095 · Cited by 1
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Nagib, M. N., Pervez, R., Nova, A. A., Ahmed, M., Maua, J., & Sayeema, A. (2025) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain66474.2025.11171893 · Cited by 1
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Babar, M., Qureshi, B., Boulila, W., & Koubâa, A. (2026) · Computer Science Review · doi:10.1016/j.cosrev.2026.100952 · Cited by 2
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Lu, Y., Tian, Y., Zhou, T., Xia, J., Zhao, Z., Sun, H., Liu, K., & Li, J. (2026) · JMIR Formative Research · doi:10.2196/87306 · Cited by 1
Jahangeer, A. S., & Pichai, S. (2026) · Future Internet · doi:10.3390/fi18090480 · Cited by 1
Sun, B., Yu, M., Wu, Y., Qi, Y., Nie, X., & Xi, S. (2021) · IET Communications · doi:10.1049/cmu2.12110 · Cited by 1
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Huang, H., Sun, C., Lei, X., Mu, N., Hu, C., Chen, C., Li, H., & Li, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8067-3_5 · Cited by 1
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Wang, C., Pan, C., Gan, Q., Liu, Z., Liu, N., & Zhang, J. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5101-3_10 · Cited by 1
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Zhu, Y., Chen, L., Zhang, W., Zhang, B., Dong, C., & Qiu, J. (2025) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-239708 · Cited by 1
Li, N., Wang, R., Luo, Y., Li, Z., Xu, H., Liu, Y., Luo, R., & Ji, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9875-2_26 · Cited by 1
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Zhang, X., Xu, H., Yin, X., Xiao, M., Wu, J., & Zhou, J. (2025) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde65448.2025.00009 · Cited by 1
Electric vehicle satisfaction analysis model based on TOPSIS-entropy weight-grey relational analysis
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Nergiz, M. E. (2022) · DÜMF Mühendislik Dergisi · doi:10.24012/dumf.1130789 · Cited by 1
Shen, E. Z. (2022) · Research Square · doi:10.21203/rs.3.rs-2204255/v1 · Cited by 1
Traffic speed prediction based on spatio-temporal attention convolutional neural networkPreprint
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Xiong, W.-Z., Shen, X., Li, H.-J., & Shen, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-547121/v1
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Wang, Y., Chen, R., Rao, A., Zhong, X., & Huang, Y. H. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1952/4/042081
Metadata
- Authors
- Liu, Y., Yu, J. J. Q., Kang, J., Niyato, D., & Zhang, S.
- Affiliations
- Southern University of Science and Technology (CN)
- Nanyang Technological University (SG)
- Venue
- IEEE Internet of Things Journal
- Published
- 2020-08
- Volume
- 7
- Issue
- 8
- Pages
- 7751-7763
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3010852232
- Cited by
- 712
- References
- 58
- Citations by year
- 2026: 67 · 2025: 122 · 2024: 157 · 2023: 140 · 2022: 116 · 2021: 85 · 2020: 25
- FWCI
- 35.97
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- General Program of Guangdong Basic and Applied Basic Research Foundation (2019A1515011032)
- Guangdong Provincial Key Laboratory (2020B121201001)
- National Research Foundation (NRF), Singapore
- Singapore Energy Market Authority, Energy Resilience (NRF2017EWT-EP003-041)
- Singapore NRF (NRF2015-NRF-ISF001-2277)
- Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE (DeSTSCI2019-0007)
- A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing (RGANS1906, WASP/NTU M4082187 (4080))
- Singapore MOE Tier 2 (MOE2014-T2-2-015 ARC4/15)
- MOE Tier 1 (2017-T1-002-007 RG122/17)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Keywords
- traffic flow prediction, federated learning, privacy preservation, gated recurrent unit, FedAvg, consensus clustering, communication cost reduction, data privacy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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