DQC-ADMM: Decentralized Dynamic ADMM With Quantized and Censored Communications
DQC-ADMM: Decentralized Dynamic ADMM With Quantized and Censored Communications
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DQC-ADMM：具有量化和审查通信的去中心化动态 ADMM
DOI：
10.1109/tnnls.2021.3051638
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发表时间：
2021
影响因子：
10.4
通讯作者：
Ling, Qing
中科院分区：
文献类型：
--
作者：
Liu, Yaohua;Wu, Gang;Tian, Zhi;Ling, Qing
在分布式学习和优化中，多个计算单元的网络协调解决大规模问题。本文重点讨论分散网络上的动态优化。我们开发了一种基于交替方向乘法器（ADMM）与量化和审查的通信，称为DQC-ADMM的通信效率的算法。在算法的每个时刻，节点协作以最小化它们的时变局部目标函数的总和。通过局部迭代计算和通信，DQC-ADMM能够跟踪时变最优解。不同于传统的方法，需要在每个时间的邻居之间的确切的本地迭代的传输，我们提出了传输的信息，以及采用通信审查策略，以减少在优化过程中的通信成本。具体来说，当且仅当该值充分偏离先前传输的值时，节点将本地信息的量化版本传输到其邻居。我们从理论上证明，建议的DQC-ADMM是能够跟踪时变的最优解，受到量化和审查的通信，以及系统动态所造成的有界误差。通过数值实验，我们评估的跟踪性能和通信节省建议DQC-ADMM。
In distributed learning and optimization, a network of multiple computing units coordinates to solve a large-scale problem. This article focuses on dynamic optimization over a decentralized network. We develop a communication-efficient algorithm based on the alternating direction method of multipliers (ADMM) with quantized and censored communications, termed DQC-ADMM. At each time of the algorithm, the nodes collaborate to minimize the summation of their time-varying, local objective functions. Through local iterative computation and communication, DQC-ADMM is able to track the time-varying optimal solution. Different from traditional approaches requiring transmissions of the exact local iterates among the neighbors at every time, we propose to quantize the transmitted information, as well as adopt a communication-censoring strategy for the sake of reducing the communication cost in the optimization process. To be specific, a node transmits the quantized version of the local information to its neighbors, if and only if the value sufficiently deviates from the one previously transmitted. We theoretically justify that the proposed DQC-ADMM is capable of tracking the time-varying optimal solution, subject to a bounded error caused by the quantized and censored communications, as well as the system dynamics. Through numerical experiments, we evaluate the tracking performance and communication savings of the proposed DQC-ADMM.