The performance of federated learning (FL) over wireless networks critically depends on accurate and timely channel state information (CSI) across distributed devices. This requirement is tightly linked to how rapidly the channel gains vary, i.e., the coherence intervals. In practice, edge devices often exhibit unequal coherence times due to differences in mobility and scattering environments, leading to unequal demands for pilot signaling and channel estimation resources. Conventional FL schemes that overlook this coherence disparity can suffer from severe communication inefficiencies and training overhead. This paper proposes a coherence-aware, communication-efficient framework for joint channel training and model updating in practical wireless FL systems operating under heterogeneous fading dynamics. Focusing on downlink impairments, we introduce a resource-reuse strategy based on product superposition, enabling the parameter server to efficiently schedule both static and dynamic devices by embedding global model updates for static devices within pilot transmissions intended for mobile devices. We theoretically analyze the convergence behavior of the proposed scheme and quantify its gains in expected communication efficiency and training accuracy. Experiments demonstrate the effectiveness of the proposed framework under mobility-induced dynamics and offer useful insights for the practical deployment of FL over wireless channels.

翻译：无线网络中的联邦学习（FL）性能关键取决于分布式设备间准确且及时的信道状态信息（CSI）。这一要求与信道增益变化的快慢（即相干间隔）紧密相关。在实际场景中，由于移动性和散射环境的差异，边缘设备通常表现出不相等的相干时间，导致其对导频信号和信道估计资源的需求不均。忽略这种相干差异的传统FL方案可能遭受严重的通信低效和训练开销。本文提出了一种相干感知、通信高效的框架，用于在异构衰落动态下的实际无线FL系统中进行联合信道训练和模型更新。聚焦于下行链路损伤，我们引入了一种基于乘积叠加的资源复用策略，使参数服务器能够通过将静态设备的全局模型更新嵌入到为移动设备设计的导频传输中，高效调度静态和动态设备。我们从理论上分析了所提方案的收敛行为，并量化了其在预期通信效率和训练精度上的增益。实验证明了该框架在移动性诱导动态下的有效性，并为无线信道上FL的实际部署提供了有益的见解。