摘要
针对蜂窝车联网(Cellular-Vehicle to Everything,C-V2X)通信场景下无线信道受到干扰导致通信过程中可能存在信息丢失的情况,通过对联邦分布式随机梯度下降(Federated Learning-Distributed Stochastic Gradient Descent,FL-DSGD)进行抗干扰模型更新机制的优化以减少上述通信链路不可靠情况的影响.该方案首先建立车辆与基站的通信链路及传输模型参数;然后在通信链路不可靠导致模型参数在传输过程中部分缺失的情况下,根据链路可靠性混合权重矩阵,利用车辆上存储的本地模型以及基站存储的全局模型参与当前轮次联邦学习的模型更新,以填充丢失的模型参数.仿真结果表明:在通信链路不可靠的情况下,FL-DSGD方案达到90%的训练准确率以及85%的测试准确率所需的通信轮次约为分布式基线方案所需通信轮次的50%.
Aiming at the possible information loss in the communication process caused by interference to the wireless channel in the Cellular Vehicle to Everything(C-V2X)communication scenario,the impact of the above unreliable communication link is reduced by optimizing the anti-interference model update mechanism of the Federated Learning Distributed Stochastic Gradient Descent(FL-DSGD).Firstly,the communication link between the vehicle and the base station and the transmission model parameters are established;Then,when the communication link is unreliable,leading to partial loss of model parameters in the transmission process,according to the link reliability mixed weight matrix,the local model stored on the vehicle and the global model stored in the base station are used to participate in the model update of the current round of federated learning to fill in the missing model parameters.Simulation results show that when the communication link is unreliable,the communication rounds required for FL-DSGD scheme to achieve 90%training accuracy and 85%test accuracy are about 50%of the communication rounds required for the distributed baseline scheme.
作者
李中捷
郭海榕
邱凡
LI Zhongjie;GUO Hairong;QIU Fan(South-Central Minzu University,College of Electronic and Information Engineering,Wuhan 430074,China;South-Central Minzu University,Hubei Key Laboratory of Intelligent Wireless Communications,Wuhan 430074,China;South-Central Minzu University,Hubei Engineering Research Center of Intelligent Internet of Things Technology,Wuhan 430074,China)
出处
《中南民族大学学报(自然科学版)》
2025年第6期826-832,共7页
Journal of South-Central Minzu University(Natural Science Edition)
基金
国家自然科学基金资助项目(61379028)
湖北省自然科学基金资助项目(2022CFB905)
中央高校基本科研业务费专项资金资助(CZY23027)。
关键词
联邦学习
车联网
随机梯度下降
federated learning
vehicle to everything
stochastic gradient descent
