首页|Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

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High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility environ-ment.In order to protect data privacy and improve data learning efficiency in knowledge sharing,we pro-pose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for train-ing client data.To enhance the wireless channel quality for knowledge sharing and reduce the commu-nication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for FBL.The problem is decoupled into two convex subproblems.Aiming to improve the resource scheduling efficiency in FBL,a double Davidon-Fletcher-Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration problem.Based on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their costs.The simulation results show that the proposed FBL framework achieves better performance than the compar-ison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

Knowledge sharingInternet of VehiclesFederated learningBroad learningReconfigurable intelligent surfacesResource allocation

Xiaoming Yuan、Jiahui Chen、Ning Zhang、Qiang (John) Ye、Changle Li、Chunsheng Zhu、Xuemin Sherman Shen

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Qinhuangdao Branch Campus,Northeastern University,Qinhuangdao 066004,China

State Key Laboratory of Integrated Services Networks & the Research Institute of Smart Transportation,Xidian University,Xi'an 710071,China

Department of Electrical and Computer Engineering,University of Windsor,Windsor,ON N9B 3P4,Canada

Department of Electrical and Software Engineering,University of Calgary,Calgary,AB T2N 1N4,Canada

SUSTech Institute of Future Networks,Southern University of Science and Technology,Shenzhen 518055,China

Department of Electrical and Computer Engineering,University of Waterloo,Waterloo,ON N2L 3G1,Canada

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国家自然科学基金国家自然科学基金Science and Technology Project of Hebei Province Education Department中央高校基本科研业务费专项Open Research Project of Xidian UniversityKey Laboratory of Cognitive Radio and Information Processing,Ministry of Education,Guilin University of Electronic Technology,Ch

6237111662231020ZD2022164N2223031ISN24-08CRKL210203

2024

10.1016/j.eng.2023.04.015

工程(英文)

工程(英文)

CSTPCDEI
ISSN：2095-8099
年,卷(期)：2024.33(2)
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