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Title	Privacy Preserving Hierarchical Federated Learning Approach For Secure Urban Mobility Prediction
ID_Doc	43124
Authors	Naresh V.S.; Ayaaappa D.
Year	2025
Published	Peer-to-Peer Networking and Applications, 18, 4
DOI	http://dx.doi.org/10.1007/s12083-025-02053-1
Abstract	This paper presents a privacy-preserving federated learning framework for urban traffic flow prediction using a Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. The proposed hierarchical system model consists of vehicles, roadside units (RSUs), and a central server, enabling collaborative traffic data analysis while maintaining data privacy. The performance of the federated learning system was evaluated with and without the CKKS encryption. In the CKKS-enabled configuration, local models initially exhibit higher errors, with mean squared errors (MSE) ranging from 0.1375 to 0.1621 and negative R² values. However, after ten training rounds, the models show significant improvements, with MSE dropping to 0.0007–0.0008 and R² values exceeding 0.9820, indicating effective learning of traffic patterns. In contrast, the non-encrypted federated learning system demonstrates strong performance from the outset, with MSE values around 0.0007–0.0008 and R² values of 0.98, further improving to an MSE of 0.0003 and R² approaching 0.993 by the end of training. While the non-encrypted system achieves high accuracy, it compromises data privacy. This study highlights the importance of balancing the predictive performance and data protection in privacy-sensitive applications. The proposed framework has practical implications for urban traffic management, enabling real-time data analysis without exposing personal identifiable information. Future research directions include exploring alternative encryption methods, optimizing architectures for faster convergence, and applying the framework to other domains such as smart cities and autonomous vehicles. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.
Author Keywords	CKKS; Federated learning; Homomorphic encryption; Privacy-preserving; Traffic prediction; Urban mobility

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