FedTSE: Low-Cost Federated Learning for Privacy-Preserved Traffic State Estimation in IoV

Xiaoming Yuan1; Jiahui Chen1; Ning Zhang2; Chunsheng Zhu3; Qiang Ye4; Xuemin Sherman Shen5

10.1109/INFOCOMWKSHPS54753.2022.9798018

2022

978-1-6654-0927-8

IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

1-6

2-5 May 2022

New York, NY, USA

Traffic state estimation long short-term memory edge computing federated learning internet of vehicles.

Others

EI

20222912361215

Cost estimating ; Decision making ; Edge computing ; Learning systems ; Long short-term memory ; Motion planning ; Reinforcement learning ; Vehicle transmissions

Mechanical Transmissions:602.2 ; Digital Computers and Systems:722.4 ; Artificial Intelligence:723.4 ; Control Systems:731.1 ; Cost and Value Engineering; Industrial Economics:911 ; Management:912.2

IEEE

1.Northeastern University,Qinhuangdao Branch Campus,Qinhuangdao,China,066004
2.University of Windsor,Department of Electrical and Computer Engineering,Windsor,ON,Canada,N9B 3P4
3.SUSTech Institute of Future Networks, Southern University of Science and Technology,Shenzhen,China,518055
4.Memorial University of Newfoundland,Department of Computer Science,St. John’s,NL,Canada,A1B 3X5
5.University of Waterloo,Department of Electrical and Computer Engineering,Waterloo,Canada,N2L 3G1

Xiaoming Yuan,Jiahui Chen,Ning Zhang,et al. FedTSE: Low-Cost Federated Learning for Privacy-Preserved Traffic State Estimation in IoV[C],2022:1-6.