ArticleScientific reports2025
FedGDAN: Privacy-preserving traffic flow prediction via federated graph diffusion attention networks.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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 spatiotemporal dependencies while maintaining data privacy and locality. To overcome these limitations, we propose FedGDAN, a Federated Graph Diffusion Attention Network that combines graph neural networks (GNNs) with federated learning (FL) to enable collaborative traffic flow prediction without sharing raw data. FedGDAN models global spatiotemporal correlations across road networks and introduces an adaptive local aggregation mechanism to address non-independent and identically data distributions, thereby improving robustness and accuracy. Experiments on real-world datasets show that FedGDAN consistently outperforms state-of-the-art centralized and federated baselines, achieving 3%-10% gains in Mean Absolute Error.
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