Federated Learning for Ultra-Reliable Low-Latency V2V Communications

TL;DR

This paper introduces a federated learning-based approach for joint power and resource allocation in vehicular networks to achieve ultra-reliable low-latency communication, significantly reducing queue extremes and power use.

Contribution

It proposes a decentralized method using federated learning and extreme value theory to estimate tail distributions of queue lengths for URLLC in V2V communications.

Findings

FL achieves near-centralized estimation accuracy with 79% less data exchange.

Method reduces large queue events by up to 60% without extra power.

Extreme event reduction is about 100 times better than queue stability-focused systems.

Abstract

In this paper, a novel joint transmit power and resource allocation approach for enabling ultra-reliable low-latency communication (URLLC) in vehicular networks is proposed. The objective is to minimize the network-wide power consumption of vehicular users (VUEs) while ensuring high reliability in terms of probabilistic queuing delays. In particular, a reliability measure is defined to characterize extreme events (i.e., when vehicles' queue lengths exceed a predefined threshold with non-negligible probability) using extreme value theory (EVT). Leveraging principles from federated learning (FL), the distribution of these extreme events corresponding to the tail distribution of queues is estimated by VUEs in a decentralized manner. Finally, Lyapunov optimization is used to find the joint transmit power and resource allocation policies for each VUE in a distributed manner. The proposed solution is validated via extensive simulations using a Manhattan mobility model. It is shown that FL enables the proposed distributed method to estimate the tail distribution of queues with an accuracy that is very close to a centralized solution with up to 79\% reductions in the amount of data that need to be exchanged. Furthermore, the proposed method yields up to 60\% reductions of VUEs with large queue lengths, without an additional power consumption, compared to an average queue-based baseline. Compared to systems with fixed power consumption and focusing on queue stability while minimizing average power consumption, the reduction in extreme events of the proposed method is about two orders of magnitude.