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).

自动驾驶领域的研究中，利用人工智能和机器学习进行车联网联合学习，通过智能车辆收集到的大量数据，在保护数据隐私和优化通信资源使用的同时，通过调度车辆的计算和传输，利用估算的无线电环境地图，协调全球模型学习，并达到降低训练时间和提高模型更新效率的目标。