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.
翻译:联邦学习(FL)作为前景良好的边际学习框架,可以通过在中央服务器的装置和模型聚合中进行分布式学习,有效地解决长期和隐私问题。为了能够实现高效的无线数据汇总,最近提议并立即引起注意。然而,无线频道的消退可以在基于空气合成的FL计划中产生综合扭曲。为了消除这一影响,在这项工作中提出了动态学习率的概念。我们开始讨论,首先考虑多输入-单输出(MISO)方案,因为潜在的优化问题是混凝土,而且有封闭式解决办法。我们随后将研究扩大到更普遍的多投入-多输出(MIMO)案例和迭接方法。广泛的模拟结果表明拟议的计划在减少总扭曲和保证测试准确性方面的有效性,使用MNIST和CIFAR10数据集。此外,我们介绍了多输入式分析,并提供了一种近于优化的封闭式设计解决方案,通过模拟进行数字验证。