  Cooperative perception plays a vital role in extending a vehicle's sensing
range beyond its line-of-sight. However, exchanging raw sensory data under
limited communication resources is infeasible. Towards enabling an efficient
cooperative perception, vehicles need to address the following fundamental
question: What sensory data needs to be shared?, at which resolution?, and with
which vehicles? To answer this question, in this paper, a novel framework is
proposed to allow reinforcement learning (RL)-based vehicular association,
resource block (RB) allocation, and content selection of cooperative perception
messages (CPMs) by utilizing a quadtree-based point cloud compression
mechanism. Furthermore, a federated RL approach is introduced in order to speed
up the training process across vehicles. Simulation results show the ability of
the RL agents to efficiently learn the vehicles' association, RB allocation,
and message content selection while maximizing vehicles' satisfaction in terms
of the received sensory information. The results also show that federated RL
improves the training process, where better policies can be achieved within the
same amount of time compared to the non-federated approach.
