Federated learning (FL) is a promising approach to enable the future Internet of vehicles consisting of intelligent connected vehicles (ICVs) with powerful sensing, computing and communication capabilities. We consider a base station (BS) coordinating nearby ICVs to train a neural network in a collaborative yet distributed manner, in order to limit data traffic and privacy leakage. However, due to the mobility of vehicles, the connections between the BS and ICVs are short-lived, which affects the resource utilization of ICVs, and thus, the convergence speed of the training process. In this paper, we propose an accelerated FL-ICV framework, by optimizing the duration of each training round and the number of local iterations, for better convergence performance of FL. We propose a mobility-aware optimization algorithm called MOB-FL, which aims at maximizing the resource utilization of ICVs under short-lived wireless connections, so as to increase the convergence speed. Simulation results based on the beam selection and the trajectory prediction tasks verify the effectiveness of the proposed solution.
翻译:联邦学习(FL)是使具有强大感测、计算和通信能力的智能连通车辆(ICV)组成的车辆能够进入未来互联网的一个很有希望的方法,我们认为一个基地站(BS)协调附近的ICV,以合作但分散的方式培训神经网络,以限制数据流量和隐私泄漏,然而,由于车辆的机动性,BS和ICV之间的联系是短暂的,影响到ICV的资源利用,从而影响到培训过程的趋同速度。在本文件中,我们提议加速FL-ICV框架,通过优化每轮培训的时间和本地迭代的数量,提高FL的趋同性能。我们建议采用机动-aware优化算法,称为MOB-FL,其目的是最大限度地利用短寿命无线连接下的ICV的资源,以便提高聚合速度。根据选择波束和轨迹预测任务,模拟FL-ICV结果,以核实拟议解决办法的有效性。