Assisted and autonomous driving are rapidly gaining momentum, and will soon become a reality. Among their key enablers, artificial intelligence and machine learning are expected to play a prominent role, also thanks to the massive amount of data that smart vehicles will collect from their onboard sensors. In this domain, federated learning is one of the most effective and promising techniques for training global machine learning models, while preserving data privacy at the vehicles and optimizing communications resource usage. In this work, we propose VREM-FL, a computation-scheduling co-design for vehicular federated learning that leverages mobility of vehicles in conjunction with estimated 5G radio environment maps. VREM-FL jointly optimizes the global model learned at the server while wisely allocating communication resources. This is achieved by orchestrating local computations at the vehicles in conjunction with the transmission of their local model updates in an adaptive and predictive fashion, by exploiting radio channel maps. The proposed algorithm can be tuned to trade model training time for radio resource usage. Experimental results demonstrate the efficacy of utilizing radio maps. VREM-FL outperforms literature benchmarks for both a linear regression model (learning time reduced by 28%) and a deep neural network for a semantic image segmentation task (doubling the number of model updates within the same time window).

翻译：辅助驾驶与自动驾驶正快速发展，即将成为现实。作为关键赋能技术之一，人工智能和机器学习预计将发挥重要作用，这得益于智能车辆通过车载传感器收集的海量数据。在该领域，联邦学习是训练全局机器学习模型的最有效且最有前景的技术之一，能够在保护车辆数据隐私的同时优化通信资源利用。本文提出VREM-FL——一种面向车辆联邦学习的计算调度协同设计方案，该方法利用车辆移动性并结合估计的5G无线电环境地图。VREM-FL通过智能分配通信资源，联合优化服务器端学习的全局模型。具体实现方式为：利用无线电信道地图，以自适应和预测的方式协调车辆本地计算与局部模型更新的传输。所提算法可进行调优，在模型训练时间与无线资源使用之间实现平衡。实验结果表明了利用无线电地图的有效性。在线性回归模型（学习时间减少28%）和语义图像分割任务的深度神经网络（相同时窗内模型更新次数翻倍）中，VREM-FL均优于文献中的基准方法。