Coherence-Aware Distributed Learning under Heterogeneous Downlink Impairments

TL;DR

This paper introduces a coherence-aware framework for federated learning over wireless networks with heterogeneous channel dynamics, improving communication efficiency and training accuracy by joint channel training and model updating.

Contribution

It proposes a novel resource-reuse strategy based on product superposition that adapts to different coherence times of devices, enhancing FL performance under realistic wireless conditions.

Findings

Theoretical analysis confirms convergence under the proposed scheme.

Experiments show significant gains in communication efficiency.

Framework effectively handles mobility-induced channel variations.

Abstract

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.