FedGDAN: Privacy-preserving traffic flow prediction via federated graph diffusion attention networks

Abstract Efficient data utilization and strong privacy protection are major challenges in Intelligent Transportation Systems (ITS), particularly in complex environments with highly distributed Intelligent Connected Vehicles (ICVs). Conventional machine learning methods struggle to capture complex sp...

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Detalles Bibliográficos

Principais autores:	Yuanhui Li, Bo Mi, Ran Zeng
Formato:	Artigo
Idioma:	Inglês
Publicado:	Nature Portfolio 2025-11-01
Series:	Scientific Reports
Assuntos:	Federated learning Graph neural network Intelligent transportation systems Privacy Traffic prediction
Acceso en liña:	https://doi.org/10.1038/s41598-025-24963-z
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