Over-the-air federated learning (FL) leverages the superposition property of multiple-access channels to enable communication-efficient distributed model training. Existing integrated sensing, communication, and computation (ISCC)-enabled over-the-air FL systems typically require dedicated resources for the sensing module, inevitably compromising FL performance due to resource competition. In this paper, we propose a sensing-native over-the-air FL framework that explores built-in distributed wireless sensing capability with zero overhead per model aggregation. Specifically, the high-dimensional local gradient signals possessing favorable autocorrelation property are concurrently leveraged for target distance estimation, while the gradient statistics already required for over-the-air FL serve as a ready-made gateway to deliver locally-sensed results to the edge server for cooperative localization. To combat inter-device interference, channel fading, and communication noise, we put forth a robust trilateration-based target positioning method building upon an efficient matched-filtering-based distance estimation. Then, by explicitly characterizing the impact of imperfect model aggregation and noisy gradient-statistics transmission on the sensing-native over-the-air FL convergence, we develop a statistics-aware communication-learning co-design approach. We first derive the closed-form optimal power budgets allocated to local gradients and their statistics, based on which an efficient successive convex approximation method is proposed for receiver beamforming optimization. Simulation results show that the proposed framework simultaneously achieves superior learning and sensing performance compared to representative baselines.

翻译：空中联邦学习利用多址信道的叠加特性实现通信高效的分布式模型训练。现有集成感知、通信与计算的空中联邦学习系统通常需要为感知模块分配专用资源，这不可避免因资源竞争而损害联邦学习性能。本文提出一种感知原生的空中联邦学习框架，以零模型聚合开销探索内置分布式无线感知能力。具体而言，具有良好自相关特性的高维局部梯度信号被同时用于目标距离估计，而空中联邦学习所需的梯度统计量则作为现成网关，将本地感知结果传输至边缘服务器以实现协作定位。为抑制设备间干扰、信道衰落和通信噪声，我们提出一种基于高效匹配滤波距离估计的鲁棒三边定位方法。进而，通过明确表征不完美模型聚合和含噪梯度统计量传输对感知原生空中联邦学习收敛性的影响，我们发展了一种统计量感知的通信-学习协同设计方法。首先推导出分配给局部梯度及其统计量的最优闭式功率预算，并据此提出一种高效逐次凸逼近方法以实现接收波束成形优化。仿真结果表明，与代表性基线相比，所提框架同时实现了优越的学习与感知性能。