Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting

doi:10.1109/TITS.2024.3429533

	Online Spatio-Temporal Correlation-Based Federated Learning for Traffic Flow Forecasting
	Liu, Qingxiang 1,2; Sun, Sheng 1; Liu, Min 1,3; Wang, Yuwei 1; Gao, Bo 4
	2024-07-31
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN	1524-9050
页码	13
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.
关键词	Predictive models Forecasting Servers Correlation Data models Federated learning Adaptation models online learning spatio-temporal correlation traffic flow forecasting
DOI	10.1109/TITS.2024.3429533
收录类别	SCI
语种	英语
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
WOS类目	Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:001283752900001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39700
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.