Sensing-Native Over-the-Air Federated Learning
感知原生的空中联邦学习
Peiyuan Huang, Shijian Gao, Jia Yan, Georgios B. Giannakis
AI总结 提出一种感知原生空中联邦学习框架,利用本地梯度信号的自相关特性实现零开销分布式感知,并通过鲁棒定位方法和统计感知的通信-学习协同设计,同时提升学习与感知性能。
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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。现有的集成感知、通信与计算(ISCC)的空中联邦学习系统通常需要为感知模块分配专用资源,由于资源竞争不可避免地损害联邦学习性能。本文提出一种感知原生的空中联邦学习框架,探索内置的分布式无线感知能力,且每次模型聚合的额外开销为零。具体地,具有良好自相关特性的高维本地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的网关,将本地感知结果传递给边缘服务器进行协作定位。为对抗设备间干扰、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。然后,通过明确刻画不完美模型聚合和带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,我们开发了一种统计感知的通信-学习协同设计方法。首先推导分配给本地梯度及其统计量的闭式最优功率预算,并基于此提出一种高效的逐次凸近似方法用于接收波束赋形优化。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和感知性能。
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.