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FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction
Yang, Linghua1; Chen, Wantong1; He, Xiaoxi2; Wei, Shuyue1; Xu, Yi3; Zhou, Zimu4; Tong, Yongxin1
2024-08-24
Conference NameKDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Source PublicationKDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Pages6105–6116
Conference DateAugust 25-29, 2024
Conference PlaceBarcelona
CountrySpain
Publication PlaceNew York, NY, USA
PublisherAssociation for Computing Machinery
Abstract

Graph-based methods have witnessed tremendous success in traffic prediction, largely attributed to their superior ability in capturing and modeling spatial dependencies. However, urban-scale traffic data are usually distributed among various owners, limited in sharing due to privacy restrictions. This fragmentation of data severely hinders interaction across clients, impeding the utilization of inter-client spatial dependencies. Existing studies have yet to address this non-trivial issue, thereby leading to sub-optimal performance. To fill this gap, we propose FedGTP, a new federated graph-based traffic prediction framework that promotes adaptive exploitation of inter-client spatial dependencies to recover close-to-optimal performance complying with privacy regulations like GDPR. We validate FedGTP via large-scale application-driven experiments on real-world datasets. Extensive baseline comparison, ablation study and case study demonstrate that FedGTP indeed surpasses existing methods through fully recovering inter-client spatial dependencies, achieving 21.08%, 13.48%, 19.90% decrease on RMSE, MAE and MAPE, respectively. Our code is available at https://github.com/LarryHawkingYoung/KDD2024_FedGTP

KeywordFederated Learning Traffic Prediction Spatial-temporal Graph Neural Network
DOI10.1145/3637528.3671613
URLView the original
Language英語English
Scopus ID2-s2.0-85203688672
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Citation statistics
Document TypeConference paper
CollectionFaculty of Science and Technology
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorHe, Xiaoxi; Tong, Yongxin
Affiliation1.SKLCCSE Lab, Beihang University Beijing, China
2.Faculty of Science and Technology, University of Macau Macau, China
3.SKLCCSE Lab, Institute of Artificial Intelligence, Beihang University Beijing, China
4.School of Data Science, City University of Hong Kong Hong Kong, China
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Yang, Linghua,Chen, Wantong,He, Xiaoxi,et al. FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction[C], New York, NY, USA:Association for Computing Machinery, 2024, 6105–6116.
APA Yang, Linghua., Chen, Wantong., He, Xiaoxi., Wei, Shuyue., Xu, Yi., Zhou, Zimu., & Tong, Yongxin (2024). FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction. KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 6105–6116.
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