Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications (1807.08127v3)

Abstract: 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.

• The paper introduces a distributed federated learning model that employs EVT and Lyapunov optimization to minimize VUE queue lengths and enhance energy efficiency.
• It develops an asynchronous FL-based MLE algorithm that reduces synchronization overhead and scales effectively in dense vehicular networks.
• Numerical evaluations demonstrate up to a 60% reduction in extreme queue lengths and a twofold decrease in power consumption, validating the method's reliability.

Overview of Distributed Federated Learning for Ultra-Reliable Low-Latency Vehicular Communications

This paper addresses the critical issue of joint power and resource allocation (JPRA) for ultra-reliable low-latency communication (URLLC) in vehicular networks, presenting a novel distributed approach utilizing federated learning (FL). The emphasis is on minimizing vehicular user equipment (VUE) queue lengths and maximizing energy efficiency while adhering to stringent requirements of low latency and high reliability. This challenge is tackled by introducing a decentralized framework, leveraging extreme value theory (EVT) to accurately characterize the tail distribution of queue lengths and employing Lyapunov optimization for deriving JPRA policies.

Main Contributions

• Extreme Value Theory (EVT) Application: The paper innovatively uses EVT to model the distribution of extreme queue lengths, which are instrumental for maintaining the expected end-to-end latency within the desired 1ms URLLC target. EVT provides the statistical foundation to characterize these rare events, offering a probabilistic measure that surpasses traditional approaches predominantly focusing on average queuing constraints. This EVT-based modeling leads to defining local constraints on queue lengths for each VUE, aiding in the effective management of network reliability.
• Federated Learning Integration: Employing FL, the method facilitates the distributed estimation of the generalized Pareto distribution's (GPD's) parameters, allowing each VUE to independently learn its local queue distribution. Unlike centralized solutions, this decentralized method uses the local gradient sharing mechanism of FL to minimize the communication cost with the central node, successfully extending the concept of URLLC to scalable vehicular networks.
• Asynchronous Model for Real-Time Systems: A notable aspect is the development of an asynchronous FL-based MLE algorithm that obviates the need for synchronized communication among VUEs, thereby curbing the overheads typically associated with centralized schemes. This approach proves beneficial in dense V2V networks with frequent real-time data exchange, allowing each VUE to asynchronously update its queue statistics.

Numerical Evaluation and Results

Simulation results using a detailed Manhattan mobility model show tangible improvements in network performance. The proposed federated approach achieves significant reliability enhancements with up to a 60% reduction in the number of VUEs with large queue lengths, thereby considerably reducing the worst-case latency metrics compared to baseline models. Furthermore, it demonstrated a twofold reduction in average power consumption, highlighting the model's energy efficiency. The numerical results further corroborate the method's reliability, achieving more than 28% and 33% in reductions of the mean and fluctuations of extreme queue lengths respectively, compared to systems that consider average queue length-based probabilistic constraints.

Theoretical and Practical Implications

Theoretically, this paper demonstrates the successful application of EVT within the domain of vehicular networks, marking a progressive step in effectively addressing the challenges of extreme queuing events in URLLC scenarios. Practically, it underscores a promising avenue for deploying FL in vehicular communications, which emphasizes a significant stride towards achieving decentralized network management with reduced data exchange overheads. The comprehensive integration of EVT and FL establishes a potent URLLC paradigm that inherently scales with network density while mitigating the confounding impacts of interference and latency.

Future Directions

As vehicular networks continue to burgeon, future exploration may include expanding FL capability to handle non-IID queued data, optimizing resource allocation further to accommodate diverse traffic patterns, and exploring cross-layer designs that synergize FL with advanced vehicular network protocols.

In summary, this paper presents an innovative and computationally efficient solution for URLLC in vehicular networks, leveraging advances in EVT and distributed learning principles. Through astutely designed simulations and theoretical underpinnings, it marks significant progress towards achieving seamless and reliable connectivity for future autonomous vehicular networks.