  A platoon-based driving is a technology allowing vehicles to follow each
other at close distances to, e.g., save fuel. However, it requires reliable
wireless communications to adjust their speeds. Recent studies have shown that
the frequency band dedicated for vehicle-to-vehicle communications can be too
busy for intra-platoon communications. Thus it is reasonable to use additional
spectrum resources, of low occupancy, i.e., secondary spectrum channels. The
challenge is to model the interference in those channels to enable proper
channel selection. In this paper, we propose a two-layered Radio Environment
Map (REM) that aims at providing platoons with accurate location-dependent
interference models by using the Federated Learning approach. Each platoon is
equipped with a Local REM that is updated on the basis of raw interference
samples and previous interference model stored in the Global REM. The model in
global REM is obtained by merging models reported by platoons. The nodes
exchange only parameters of interference models, reducing the required control
channel capacity. Moreover, in the proposed architecture platoon can utilize
Local REM to predict channel occupancy, even when the connection to the Global
REM is temporarily unavailable. The proposed system is validated via computer
simulations considering non-trivial interference patterns.
