  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.
