FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction-Reference-Cited by-同舟云学术

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FedGTP: Exploiting Inter-Client Spatial Dependency in Federated Graph-based Traffic Prediction

Published:2024-08-24 Issue: Volume:52 Page:6105-6116
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Yang Linghua¹^ORCID,Chen Wantong¹^ORCID,He Xiaoxi²^ORCID,Wei Shuyue¹^ORCID,Xu Yi³^ORCID,Zhou Zimu⁴^ORCID,Tong Yongxin¹^ORCID

Affiliation:

1. 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

Funder

Beihang University Basic Research Funding

National Science Foundation of China (NSFC)

Didi Collaborative Research Program

Beijing Natural Science Foundation

Chow Sang Sang Group Research Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671613

Reference55 articles.

1. A Survey on Homomorphic Encryption Schemes

2. Lei Bai Lina Yao Salil S. Kanhere Xianzhi Wang et al. 2019. STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. In IJCAI. ijcai.org 1981--1987.

3. Passenger Demand Forecasting with Multi-Task Convolutional Recurrent Neural Networks

4. Lei Bai Lina Yao Can Li Xianzhi Wang et al. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. In NeurIPS.

5. Weiqi Chen Ling Chen Yu Xie Wei Cao et al. 2020. Multi-Range Attentive Bicomponent Graph Convolutional Network for Traffic Forecasting. In AAAI. AAAI Press 3529--3536.

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