Federated learning (FL) as a promising edge-learning framework can
effectively address the latency and privacy issues by featuring distributed
learning at the devices and model aggregation in the central server. In order
to enable efficient wireless data aggregation, over-the-air computation
(AirComp) has recently been proposed and attracted immediate attention.
However, fading of wireless channels can produce aggregate distortions in an
AirComp-based FL scheme. To combat this effect, the concept of dynamic learning
rate (DLR) is proposed in this work. We begin our discussion by considering
multiple-input-single-output (MISO) scenario, since the underlying optimization
problem is convex and has closed-form solution. We then extend our studies to
more general multiple-input-multiple-output (MIMO) case and an iterative method
is derived. Extensive simulation results demonstrate the effectiveness of the
proposed scheme in reducing the aggregate distortion and guaranteeing the
testing accuracy using the MNIST and CIFAR10 datasets. In addition, we present
the asymptotic analysis and give a near-optimal receive beamforming design
solution in closed form, which is verified by numerical simulations.

本文提出了一种动态学习率（DLR）的机制以改善基于无线计算的联邦学习（FL）中由信道衰落引起的聚合失真问题，并通过基于 MNIST 和 CIFAR10 数据集的实验结果证明了该方案的有效性及其近似最优解的收敛性。