Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
文献类型：期刊论文
| 作者 | Liu, Qingxiang1,2; Sun, Sheng1; Liu, Min1,3; Wang, Yuwei1; Gao, Bo4 |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2024-07-31 |
| 页码 | 13 |
| 关键词 | Predictive models Forecasting Servers Correlation Data models Federated learning Adaptation models online learning spatio-temporal correlation traffic flow forecasting |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2024.3429533 |
| 英文摘要 | Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent Transportation Systems. To mitigate communication burden and tackle with the problem of privacy leakage aroused by centralized forecasting methods, Federated Learning (FL) has been applied to TFF. However, existing FL-based approaches employ batch learning manner, which makes the pre-trained models inapplicable to subsequent traffic data, thus exhibiting subpar prediction performance. In this paper, we perform the first study of forecasting traffic flow adopting online learning manner in FL framework and then propose a novel prediction method named Online Spatio-Temporal Correlation-based Federated Learning (FedOSTC), aiming to guarantee performance gains regardless of traffic fluctuation. Specifically, clients employ Gated Recurrent Unit (GRU)-based encoders to obtain the internal temporal patterns inside traffic data sequences. Then, the central server evaluates spatial correlation among clients via Graph Attention Network (GAT), catering to the dynamic changes of spatial closeness caused by traffic fluctuation. Furthermore, to improve the generalization of the global model for upcoming traffic data, a period-aware aggregation mechanism is proposed to aggregate the local models which are optimized using Online Gradient Descent (OGD) algorithm at clients. We perform comprehensive experiments on two real-world datasets to validate the efficiency and effectiveness of our proposed method and the numerical results demonstrate the superiority of FedOSTC. |
| 资助项目 | National Key Research and Development Program of China[2021YFB2900102] ; National Natural Science Foundation of China[62072436] ; National Natural Science Foundation of China[62202449] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:001283752900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/39699]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, Min |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Zhongguancun Lab, Beijing 100094, Peoples R China 4.Beijing Jiaotong Univ, Sch Comp Sci & Technol, Beijing 100044, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Qingxiang,Sun, Sheng,Liu, Min,et al. Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2024:13. |
| APA | Liu, Qingxiang,Sun, Sheng,Liu, Min,Wang, Yuwei,&Gao, Bo.(2024).Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13. |
| MLA | Liu, Qingxiang,et al."Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2024):13. |
入库方式： OAI收割
来源：计算技术研究所
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