Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computation

TL;DR

This paper introduces a dynamic learning rate scheme for federated learning over wireless channels using over-the-air computation, effectively reducing distortion and improving accuracy in noisy wireless environments.

Contribution

It proposes a novel dynamic learning rate method tailored for AirComp-based federated learning, with solutions for MISO and MIMO scenarios and asymptotic analysis for near-optimal beamforming.

Findings

Reduces aggregate distortion in AirComp FL

Improves testing accuracy on MNIST and CIFAR10 datasets

Provides closed-form near-optimal beamforming solutions

Abstract

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.