Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization
Data Shuffling in Wireless Distributed Computing via Low-Rank Optimization
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DOI：
10.1109/tsp.2019.2912139
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发表时间：
2018-09
影响因子：
5.4
通讯作者：
Kai Yang;Yuanming Shi;Z. Ding
中科院分区：
文献类型：
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作者：
Kai Yang;Yuanming Shi;Z. Ding
智能移动平台，如智能车辆和无人机，最近已成为在车载环境中部署机器学习机制的关注焦点，以便在隐私泄露风险较低的情况下做出低延迟决策。然而，大多数此类机器学习算法对计算和内存的要求都很高，这使得在计算、内存和能源资源有限的单一设备上进行必要的计算变得非常困难。无线分布式计算通过整合设备间的计算和存储资源带来了新的机遇。对于低延迟应用，关键瓶颈在于移动设备之间为数据混洗而进行的中间结果交换。为了提高通信效率，我们提出了一种同信道通信模型，并通过利用本地计算的中间值作为辅助信息来设计收发器。提出了一种低秩优化模型，通过为数据混洗建立干扰对齐条件来最大化实现的自由度（DoF）。不幸的是，由于所构建的低秩优化问题结构不佳，现有的近似秩函数的方法无法取得令人满意的性能。在本文中，我们通过为秩函数提出一种新的凸函数差（DC）表示，开发了一种有效的凸函数差算法来解决所提出的低秩优化问题。数值实验表明，所提出的DC方法可以显著提高通信效率，并且当移动设备数量增加时，可实现的自由度几乎保持不变。
Intelligent mobile platforms such as smart vehicles and drones have recently become the focus of attention for onboard deployment of machine learning mechanisms to enable low latency decisions with low risk of privacy breach. However, most such machine learning algorithms are both computation-and-memory intensive, which makes it highly difficult to implement the requisite computations on a single device of limited computation, memory, and energy resources. Wireless distributed computing presents new opportunities by pooling the computation and storage resources among devices. For low-latency applications, the key bottleneck lies in the exchange of intermediate results among mobile devices for data shuffling. To improve communication efficiency, we propose a co-channel communication model and design transceivers by exploiting the locally computed intermediate values as side information. A low-rank optimization model is proposed to maximize the achieved degrees-of-freedom (DoF) by establishing the interference alignment condition for data shuffling. Unfortunately, existing approaches to approximate the rank function fail to yield satisfactory performance due to the poor structure in the formulated low-rank optimization problem. In this paper, we develop an efficient difference-of-convex-functions (DC) algorithm to solve the presented low-rank optimization problem by proposing a novel DC representation for the rank function. Numerical experiments demonstrate that the proposed DC approach can significantly improve the communication efficiency whereas the achievable DoF almost remains unchanged when the number of mobile devices grows.