车联网联邦学习（VFL）为智能交通系统提供了隐私保护的协同模型训练能力，现有研究已探索通信资源分配和梯度稀疏化技术以降低通信开销。然而，车辆移动性导致信道条件和传输容量快速变化，使得预设的资源分配和稀疏化决策失效。本文提出 FedPGT，一种针对时变信道下 VFL 的渐进式梯度传输方案，车辆根据瞬时信道条件逐步传输高幅值梯度元素。我们建立了收敛界，刻画了传输梯度元素的影响，并揭示了由幂律衰减支配的收益递减行为。基于此结果，我们将在线决策问题建模为随机优化问题，其主要挑战在于目标函数具有累积耦合且不可分离的特性。为解决这一挑战，我们引入逐时隙代理传输变量以解耦跨时隙的长期依赖关系，并将原始目标转化为可加的逐时隙优化问题，从而支持采用 Lyapunov 漂移加惩罚方法进行在线调度。此外，我们开发了一种低复杂度资源分配算法以实现高效的在线部署。实验结果表明，与最先进基线相比，所提方案在 CIFAR-10 图像分类任务上实现了 3.65% 的准确率提升，在 Argoverse 轨迹预测任务上将平均位移误差降低了 12.66%，证明了其在高度动态车辆环境中适用于多样化学习任务的能力。

Vehicular federated learning (VFL) enables privacy-preserving collaborative model training for intelligent transportation systems, where communication resource allocation and gradient sparsification techniques have been explored to reduce communication overhead. However, vehicle mobility leads to rapidly varying channel conditions and transmission capacity, rendering predetermined resource allocation and sparsification decisions ineffective. In this paper, we propose FedPGT, a progressive gradient transmission scheme for VFL over time-varying channels, where vehicles progressively transmit high-magnitude gradient entries in response to instantaneous channel conditions. We establish a convergence bound that characterizes the impact of transmitted gradient entries and reveals diminishing-return behavior governed by a power-law decay. Motivated by this result, we formulate a stochastic optimization problem for online decision-making, where the main challenge lies in a cumulatively coupled, non-separable objective. To handle this challenge, we introduce per-slot surrogate transmission variables to decouple the long-term dependence across time slots and convert the original objective into an additive per-slot optimization problem, enabling a Lyapunov drift-plus-penalty approach for online scheduling. We further develop a low-complexity resource allocation algorithm for efficient online implementation. Experimental results demonstrate that the proposed scheme achieves a 3.65% accuracy improvement on the CIFAR-10 image classification task and a 12.66% reduction in average displacement error on the Argoverse trajectory prediction task compared with state-of-the-art baselines, demonstrating its applicability to diverse learning tasks under highly dynamic vehicular environments.