FedGRU: Privacy-preserving Traffic Flow Prediction via Federated Learning

Yi Liu1,2; Shuyu Zhang1; Chenhan Zhang1; James J.Q. Yu1

10.1109/ITSC45102.2020.9294453

2020-12-24

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

2153-0009

978-1-7281-4150-3

2020 IEEE 23RD INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)

3517-3522

20-23 Sept. 2020

Rhodes, Greece

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Existing traffic flow forecasting technologies achieve great success based on deep learning models on a large number of datasets gathered by organizations. However, there are two critical challenges. One is that data exists in the form of "isolated islands - . The other is the data privacy and security issue, which is becoming more significant than ever before. In this paper, we propose a Federated Learning-based Gated Recurrent Unit neural network framework (FedGRU) for traffic flow prediction (TFP) to address these challenges. Specifically, FedGRU model differs from current centralized learning methods and updates a universe learning model through a secure aggregation parameter mechanism rather than sharing data among organizations. In the secure parameter aggregation mechanism, we introduce a Federated Averaging algorithm to control the communication overhead during parameter transmission. Through extensive case studies on the Performance Measurement System (PeMS) dataset, it is shown that FedGRU model can achieve accurate and timely traffic prediction without compromising privacy.

Organizations Data models Data privacy Predictive models Privacy Logic gates Computational modeling

第一 ; 通讯

英语

CPCI-S ; EI

General Program of Guangdong Basic and Applied Basic Research Foundation[2019A1515011032]

Engineering ; Transportation

Engineering, Electrical & Electronic ; Transportation Science & Technology

WOS:000682770701114

20210409824478

Data Sharing ; Forecasting ; Intelligent systems ; Intelligent vehicle highway systems ; Large dataset ; Privacy by design ; Recurrent neural networks ; Traffic control

Artificial Intelligence:723.4 ; Computer Applications:723.5

人工提交

1.Guangdong Provincial Key Laboratory of Braininspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
2.School of Data Science and Technology, Heilongjiang University, Harbin, China

Yi Liu,Shuyu Zhang,Chenhan Zhang,et al. FedGRU: Privacy-preserving Traffic Flow Prediction via Federated Learning[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:3517-3522.