Federated Learning-based Privacy-Preserving Traffic Flow Prediction Scheme for VANETs
Abstract
Vehicular ad hoc networks (VANETs) bring us a lot of convenience. However, the credibility of the messages transmitted among vehicles and the security of VANETs still face risks and challenges. In this paper, we propose a privacy-preserving traffic flow prediction scheme based on federated learning and long short-term memory recurrent neural network (LSTM RNN), which can perform traffic flow prediction for VANETs. We use LSTM RNN as the deep learning neural network for predicting traffic flow. Federated learning framework is used for model interaction and data privacy protection. Security and performance analysis show that the proposed scheme can not only predict the traffic flow, but also protect the privacy of users and model.
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[24] used HE to secure model parameters, preventing inference attacks for traffic flow prediction.
“Study [24] used HE to secure model parameters, preventing inference attacks for traffic flow prediction.”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [24]
Cites
Towards Fair and Privacy-Preserving Federated Deep Models
Lyu, L., Yu, J., Nandakumar, K., Li, Y., Ma, X., Jin, J., Yu, H., & Ng, K. S. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.2996273 · Cited by 250 · see citing works
Privacy-preserving and communication-efficient federated learning in Internet of Things
Fang, C., Guo, Y., Hu, Y., Ma, B., Feng, L., & Yin, A. (2021) · Computers & Security · doi:10.1016/j.cose.2021.102199 · Cited by 93 · see citing works
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Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 1 · see citing works
“It also mentions how these techniques can be applied are applied in the context of ML-based IoV applications.”
- Authors
- Deng, J., & Shen, G.
- Affiliations
- Hubei University of Technology (CN)
- ORCID
- Shen, Gang
- Venue
- 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE)
- Publisher
- IEEE
- Published
- 2022-05-27
- Pages
- 374-378
- OpenAlex
- W4297968295
- Cited by
- 12
- References
- 2
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 4
- FWCI
- 4.45
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Communications, Information System and Computer Engineering (CISCE) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Vehicular Ad Hoc Networks (VANETs)
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Keywords
- federated learning, traffic flow prediction, vehicular ad hoc networks, privacy preservation, long short-term memory, recurrent neural networks, VANET security, user privacy protection
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