Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT  

作　　者：Wugedele Bao Celimuge Wu Siri Guleng Jiefang Zhang Kok-Lim Alvin Yau Yusheng Ji 

机构地区：[1]School of computer science and information engineering,Hohhot Minzu College,Hohhot 010051,China [2]Graduate School of Informatics and Engineering,The University of Electro-Communications,1-5-1,Chofugaoka,Chofu-shi,Tokyo,182-8585 Japan [3]Institute of Intelligent Media Technology,Communication University of Zhejiang,Zhejiang 310018,China [4]School of Science and Technology,Sunway University,5,Jalan Universiti,Bandar Sunway,47500 Petaling Jaya,Selangor,Malaysia [5]Information Systems Architecture Research Division,National Institute of Informatics,2-1-2,Hitotsubashi,Chiyoda-ku,Tokyo 101-8430 Japan

出　　处：《China Communications》2021年第6期39-52,共14页中国通信（英文版）

基　　金：This research was supported in part by the National Natural Science Foundation of China under Grant No.62062031 and 61877053;in part by Inner Mongolia natural science foundation grant number 2019MS06035,and Inner Mongolia Science and Technology Major Project,China;in part by ROIS NII Open Collaborative Research 21S0601;in part by JSPS KAKENHI grant numbers 18KK0279,19H04093,20H00592,and 21H03424.

摘　　要：In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

关 键 词：vehicular IoT federated learning client selection networking scheme 

分 类 号：U495[交通运输工程—交通运输规划与管理] TN929.5[交通运输工程—道路与铁道工程] TP391.44[电子电信—通信与信息系统]