  In this paper, the problem of joint power and resource allocation (JPRA) for
ultra-reliable low-latency communication (URLLC) in vehicular networks is
studied. Therein, the network-wide power consumption of vehicular users (VUEs)
is minimized subject to high reliability in terms of probabilistic queuing
delays. Using extreme value theory, a new reliability measure is defined to
characterize extreme events pertaining to vehicles' queue lengths exceeding a
predefined threshold. To learn these extreme events, assuming they are
independently and identically distributed over VUEs, a novel distributed
approach based on federated learning (FL) is proposed to estimate the tail
distribution of the queue lengths. Considering the communication delays
incurred by FL over wireless links, Lyapunov optimization is used to derive the
JPRA policies enabling URLLC for each VUE in a distributed manner. The proposed
solution is then validated via extensive simulations using a Manhattan mobility
model. Simulation results show that FL enables the proposed method to estimate
the tail distribution of queues with an accuracy that is close to a centralized
solution with up to 79% reductions in the amount of exchanged data.
Furthermore, the proposed method yields up to 60% reductions of VUEs with large
queue lengths, while reducing the average power consumption by two folds,
compared to an average queue-based baseline.
