针对车载网络信道不完美问题，提出动态选参与方与自适应速率结合的联邦学习方法。
VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSI
- 根据车辆状态动态选参与方并调整通信速率
- 相比现有方法收敛速度提升约40%
- 适合高移动性、信道条件差的车载场景
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，主要源于车辆高速移动及信道状态信息不完善。现有方法常过度简化现实，假设固定通信轮次或理想信道条件，限制了实际应用效果。为此，我们提出面向不完美信道状态信息的可变速率车载联邦学习（VR-VFL），该方法结合动态客户端选择与自适应传输速率选择，并允许每轮时长随无线环境变化灵活调整。其核心为双目标优化框架，平衡学习收敛速度与每轮完成时间。通过考虑移动性与真实无线约束，VR-VFL在车载边缘网络中提供更实用高效的联邦学习方案。仿真结果表明，所提方案相较文献中其他方法，收敛速度提升约40%。
原文摘要 · Abstract (English)
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.
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