Federated Learning (FL) with over-the-air computation is susceptible to analog aggregation error due to channel conditions and noise. Excluding devices with weak channels can reduce the aggregation error, but also decreases the amount of training data in FL. In this work, we jointly design the uplink receiver beamforming and device selection in over-the-air FL to maximize the training convergence rate. We propose a new method termed JBFDS, which takes into account the impact of receiver beamforming and device selection on the global loss function at each training round. Our simulation results with real-world image classification demonstrate that the proposed method achieves faster convergence with significantly lower computational complexity than existing alternatives.

翻译：空中计算使能的联邦学习（FL）易受信道条件与噪声影响而产生模拟聚合误差。排除信道质量较弱的设备虽可降低聚合误差，但会减少FL中训练数据的总量。本研究针对空中联邦学习场景，联合设计上行接收波束成形与设备选择策略以最大化训练收敛速率。我们提出一种名为JBFDS的新方法，该方法在每轮训练中充分考虑接收波束成形与设备选择对全局损失函数的影响。基于真实图像分类的仿真结果表明，与现有方法相比，所提方法在显著降低计算复杂度的同时实现了更快的收敛速度。