  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).
