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

Yuan，Xiaoming1,2; Chen，Jiahui1; Zhang，Ning3; Ye，Qiang (John)4; Li，Changle2; Zhu，Chunsheng5; Sherman Shen，Xuemin6

2024

10.1016/j.eng.2023.04.015

Engineering   影响因子和分区

2095-8099

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 environment. In order to protect data privacy and improve data learning efficiency in knowledge sharing, we propose 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 training client data. To enhance the wireless channel quality for knowledge sharing and reduce the communication 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 comparison models in terms of efficiency, accuracy, and cost for knowledge sharing in the IoV.

Broad learning Federated learning Internet of Vehicles Knowledge sharing Reconfigurable intelligent surfaces Resource allocation

SCI ; EI

英语

其他

2-s2.0-85184583459

Scopus

1.Qinhuangdao Branch Campus,Northeastern University,Qinhuangdao,066004,China
2.State Key Laboratory of Integrated Services Networks & the Research Institute of Smart Transportation,Xidian University,Xi'an,710071,China
3.Department of Electrical and Computer Engineering,University of Windsor,Windsor,N9B 3P4,Canada
4.Department of Electrical and Software Engineering,University of Calgary,Calgary,AB T2N 1N4,Canada
5.SUSTech Institute of Future Networks,Southern University of Science and Technology,Shenzhen,518055,China
6.Department of Electrical and Computer Engineering,University of Waterloo,Waterloo,N2L 3G1,Canada

Yuan，Xiaoming,Chen，Jiahui,Zhang，Ning,et al. Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles[J]. Engineering,2024.

Yuan，Xiaoming.,Chen，Jiahui.,Zhang，Ning.,Ye，Qiang .,Li，Changle.,...&Sherman Shen，Xuemin.(2024).Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles.Engineering.

Yuan，Xiaoming,et al."Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles".Engineering (2024).