高速车联网场景下分簇式无线联邦学习算法

现有无线联邦学习框架缺乏对实际的分布式高速车联网（IoV）场景的有效支持。针对该场景下的分布式学习问题，提出了一种基于随机网络拓扑模型的分布式训练算法——分簇式无线联邦学习算法（C-WFLA）。首先，该算法基于高速公路场景下的车辆分布情况设计网络模型；其次，该算法考虑了用户端进行上行数据传输时的路径衰落、瑞利衰落等因素；最后，该算法设计了基于分簇式训练的无线联邦学习方法。利用所提算法对手写体识别模型进行了训练与测试，仿真结果表明：在信道状态较好、用户发射功率受限较小的情况下，传统无线联邦学习算法与C-WFLA在相同的训练条件下损失函数均能收敛至相近的数值，且C-WFLA收敛更快；而在信道状态较差、用户发射功率受限较大的情况下，C-WFLA损失函数收敛值相较于传统的集中式算法可以降低10%~50%。可见，C-WFLA更有助于高速IoV场景下的模型训练。

Clustered wireless federated learning algorithm in high-speed internet of vehicles scenes

Existing wireless federated learning frameworks lack the effective support for the actual distributed high-speed Internet of Vehicles (IoV) scenes. Aiming at the distributed learning problem in such scenes, a distributed training algorithm based on the random network topology model named Clustered-Wireless Federated Learning Algorithm (C-WFLA) was proposed. In this algorithm, firstly, a network model was designed on the basis of the distribution situation of vehicles in the highway scene. Secondly, the path fading, Rayleigh fading and other factors during the uplink data transmission of the users were considered. Finally, a wireless federated learning method based on clustered training was designed. The proposed algorithm was used to train and test the handwriting recognition model. The simulation results show that under the situations of good channel state and little user transmit power limit, the loss functions of traditional wireless federated learning algorithm and C-WFLA can converge to similar values under the same training condition, but C-WFLA converges faster; under the situations of poor channel state and much user transmit power limit, C-WFLA can reduce the convergence value of loss function by 10% to 50% compared with the traditional centralized algorithm. It can be seen that C-WFLA is more helpful for model training in high-speed IoV scenes.