将信道噪声转化为空中主成分分析的加速器
Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis
摘要：
近年来，将移动数据提取为有用知识的尝试已经导致在网络边缘部署机器学习算法。主成分分析（PCA）是一种提取数据集线性结构的经典技术，对特征提取和数据压缩有重要的应用价值。在这项工作中，我们提出了部署分布式PCA在多访问通道的基础上的算法随机梯度下降学习的主导特征空间的分布式数据集在多个设备。采用空中聚合来减少多路访问延迟，称为空中PCA。该设计的新颖性在于利用信道噪声来加速梯度下降遇到的每个鞍点周围的区域中的下降，从而提高空中PCA的收敛速度。该思想通过提出一种功率控制方案来实现，该方案检测下降区域的类型并相应地控制信道噪声的水平。该方案被证明是实现一个更快的收敛速度比在没有功率控制的情况下。
英文摘要：
Recently years, the attempts on distilling mobile data into useful knowledge has been led to the deployment of machine learning algorithms at the network edge. Principal component analysis (PCA) is a classic technique for extracting the linear structure of a dataset, which is useful for feature extraction and data compression. In this work, we propose the deployment of distributed PCA over a multi-access channel based on the algorithm of stochastic gradient descent to learn the dominant feature space of a distributed dataset at multiple devices. Over-the-air aggregation is adopted to reduce the multi-access latency, giving the name over-the-air PCA. The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA. The idea is materialized by proposing a power-control scheme which detects the type of descent region and controlling the level of channel noise accordingly. The scheme is proved to achieve a faster convergence rate than in the case without power control.
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