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Smart city article details

Title	Enhancing Urban Traffic Forecasting With Scalable, Privacy-Preserving Federated Learning Approach
ID_Doc	24076
Authors	Jiang Z.
Year	2024
Published	2024 4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024
DOI	http://dx.doi.org/10.1109/EIECT64462.2024.10866348
Abstract	Accurate traffic forecasting is crucial for managing urban congestion and reducing delays. With the expansion of urban areas, traditional traffic prediction models increasingly struggle to adapt to the complexity and variability of modern traffic patterns. This study introduces a novel approach that leverages deep learning models trained through federated learning across distributed edge devices and cloud servers, such as traffic sensors, to predict traffic patterns. The use of federated learning techniques enhances prediction performance while preserving data privacy. To address the resource constraints and communication overhead associated with federated learning, techniques such as model compression, federated averaging, and selective participation are employed. The proposed approach provides a scalable, efficient, and privacy-preserving solution for accurate traffic predictions in smart cities, facilitating proactive traffic management and urban planning. © 2024 IEEE.
Author Keywords	Data Privacy; Federated Learning; Scalability; Traffic Prediction

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