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

翻译：辅助驾驶与自动驾驶技术正迅速发展，并即将成为现实。人工智能和机器学习被视为关键推动力，这得益于智能车辆将从车载传感器收集的海量数据。联邦学习是最具前景的技术之一，它能够在训练全局机器学习模型的同时，保护车辆数据隐私并优化通信资源使用。本文提出车载无线电环境地图联邦学习（VREM-FL），一种面向车载联邦学习的计算-调度协同设计方案，它将车辆移动性与5G无线电环境地图相结合。VREM-FL联合优化了全局模型的学习性能，并智能分配通信与计算资源。这是通过利用无线电信道地图，以自适应和预测性的方式，协同调度车辆端的本地计算及其本地模型的传输来实现的。所提算法可进行调整，以在训练时间与无线电资源使用之间进行权衡。实验结果表明，无论是对于线性回归模型（学习时间减少28%），还是用于语义图像分割的深度神经网络（在相同时间窗口内模型更新次数翻倍），VREM-FL均优于文献中的基准方法。