Federated Learning for Ultra-Reliable Low-Latency V2V Communications (1805.09253v1)

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.

• The paper introduces a federated learning-based framework that minimizes VUE power consumption while meeting strict latency and reliability constraints.
• It employs Extreme Value Theory to model queue length extremes and Lyapunov optimization to derive stable, distributed resource allocation policies.
• Simulations using a Manhattan mobility model demonstrate a 60% reduction in large queue events and a 79% decrease in data exchange compared to centralized methods.

Federated Learning for Ultra-Reliable Low-Latency V2V Communications

The paper at hand tackles a crucial challenge in vehicular communications: achieving Ultra-Reliable Low-Latency Communication (URLLC) with a focus on vehicle-to-vehicle (V2V) networks in autonomous and intelligent transportation systems. The authors propose an innovative distributed approach to transmit power and resource allocation utilizing the concepts of Federated Learning (FL), Extreme Value Theory (EVT), and Lyapunov optimization. This approach is designed to minimize power consumption while maintaining stringent reliability and latency constraints.

Key Insights and Methodology

1. Problem Formulation: The paper introduces a network-wide power optimization problem where the goal is to minimize vehicular user equipment (VUE) power consumption. This goal is subject to both latency constraints and reliability measures characterized by queuing dynamics.
2. Extreme Value Theory (EVT): EVT is leveraged to model extreme events, characterized by a queue length exceeding a predefined threshold. The distribution of these extreme events is modeled using the generalized Pareto distribution (GPD), facilitating a probabilistic constraint formulation for ensuring reliability.
3. Federated Learning Approach: Instead of centrally collecting all data for analysis, FL allows VUEs to learn and update their local models of queue length distributions. The aggregated FL models enable the RSU to construct a global model, reducing the data exchange by up to 79% compared to centralized methods.
4. Lyapunov Optimization: The authors employ Lyapunov optimization to derive distributed control policies for transmission power and resource allocation. This technique ensures system stability and meets the probabilistic constraints derived from EVT.
5. Simulation and Results: Detailed simulations are conducted using a Manhattan mobility model. Results demonstrate that the proposed FL-based solution can closely estimate the tail distribution of queue lengths with high accuracy, and achieve a 60% reduction in VUEs with large queue lengths, without additional power consumption.

Implications and Future Directions

The proposed framework highlights the potential of combining FL and EVT in designing efficient resource allocation protocols for vehicular networks. The reduction in signaling overheads, achieved by imparting learning capabilities at the network edge, is a significant advantage over traditional centralized approaches.

This methodological innovation not only enhances the performance of V2V communications but also opens pathways for improvements in other URLLC-demanding scenarios like industrial automation and smart grid communications. Future research could further refine model estimation techniques for even greater efficiency and explore the integration of additional AI-driven methodologies to enhance decision-making.

The paper also paves the way for further exploration of FL in other edge-network scenarios where data privacy and reduced latency are critical, underpinning the transition towards more intelligent autonomous vehicular systems.

Overall, this research contributes to the development of scalable and reliable communication frameworks within vehicular networks, advancing both the theoretical and practical aspects of URLLC in highly dynamic environments.