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In this paper, a novel joint transmit power and resource allocation approachfor enabling ultra-reliable low-latency communication (URLLC) in vehicularnetworks is proposed. The objective is to minimize the network-wide powerconsumption of vehicular users (VUEs) while ensuring high reliability in termsof probabilistic queuing delays. In particular, a reliability measure isdefined to characterize extreme events (i.e., when vehicles' queue lengthsexceed a predefined threshold with non-negligible probability) using extremevalue theory (EVT). Leveraging principles from federated learning (FL), thedistribution of these extreme events corresponding to the tail distribution ofqueues is estimated by VUEs in a decentralized manner. Finally, Lyapunovoptimization is used to find the joint transmit power and resource allocationpolicies for each VUE in a distributed manner. The proposed solution isvalidated via extensive simulations using a Manhattan mobility model. It isshown that FL enables the proposed distributed method to estimate the taildistribution of queues with an accuracy that is very close to a centralizedsolution with up to 79\% reductions in the amount of data that need to beexchanged. Furthermore, the proposed method yields up to 60\% reductions ofVUEs with large queue lengths, without an additional power consumption,compared to an average queue-based baseline. Compared to systems with fixedpower consumption and focusing on queue stability while minimizing averagepower consumption, the reduction in extreme events of the proposed method isabout two orders of magnitude.

Federated Learning for Ultra-Reliable Low-Latency V2V Communications