Federated learning in vehicular edge networks faces major challenges in efficient resource allocation, largely due to high vehicle mobility and the presence of imperfect channel state information. Many existing methods oversimplify these realities, often assuming fixed communication rounds or ideal channel conditions, which limits their effectiveness in real-world scenarios. To address this, we propose variable rate vehicular federated learning (VR-VFL), a novel federated learning method designed specifically for vehicular networks under imperfect channel state information. VR-VFL combines dynamic client selection with adaptive transmission rate selection, while also allowing round times to flex in response to changing wireless conditions. At its core, VR-VFL is built on a bi-objective optimization framework that strikes a balance between improving learning convergence and minimizing the time required to complete each round. By accounting for both the challenges of mobility and realistic wireless constraints, VR-VFL offers a more practical and efficient approach to federated learning in vehicular edge networks. Simulation results show that the proposed VR-VFL scheme achieves convergence approximately 40% faster than other methods in the literature.

翻译：车载边缘网络中的联邦学习面临高效资源分配的重大挑战，这主要源于车辆的高移动性和非完美的信道状态信息。许多现有方法过度简化了这些现实情况，通常假设固定的通信轮次或理想的信道条件，这限制了其在真实场景中的有效性。为解决此问题，我们提出了可变速率车载联邦学习（VR-VFL），这是一种专为非完美信道状态信息下的车载网络设计的新型联邦学习方法。VR-VFL将动态客户端选择与自适应传输速率选择相结合，同时允许轮次时间根据变化的无线条件灵活调整。其核心在于，VR-VFL建立在一个双目标优化框架之上，该框架在提升学习收敛速度与最小化完成每轮所需时间之间取得了平衡。通过同时考虑移动性挑战和现实的无线约束，VR-VFL为车载边缘网络中的联邦学习提供了一种更实用、更高效的方法。仿真结果表明，所提出的VR-VFL方案比文献中的其他方法收敛速度快约40%。