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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].”
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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
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