DAC-SGD: A Distributed Stochastic Gradient Descent Algorithm Based on Asynchronous Connection

Aijia He, Zehong Chen, Weichen Li, Xingying Li, Hongjun Li, Xin Zhao
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Abstract

In the data mining practice, it happens that the algorithm used in mining tasks needs to deal with the multiple distributed data source, while the required datasets are located in different companies or organizations and reside in different system and technology environments. In traditional mining solutions or algorithms, data located in different source need to be copied and integrated into a homogenous computation environment, and then the mining can be executed, which leads to large data transmission and high storage costs. Even the data mining can be in feasible due to the data ownership problems. In this paper, a distributed asynchronous connection approach for the well-used stochastic gradient descent algorithm (SGD) was presented, and a distributed implementation for it was done to cope with the multiple distributed data source problems. In which, the main process of the algorithm was executed asynchronously in distributed computation node and the model can be trained locally in multiple data sources based on their own computation environment, so as to avoid the data integration and centralized processing. And the feasibility and performance for the proposed algorithm was evaluated based on experimental studies.
基于异步连接的分布式随机梯度下降算法DAC-SGD
在数据挖掘实践中,挖掘任务所使用的算法往往需要处理多个分布式数据源,而所需的数据集位于不同的公司或组织中,处于不同的系统和技术环境中。在传统的挖掘解决方案或算法中,需要将不同来源的数据复制并集成到一个同质的计算环境中,然后才能进行挖掘,这导致数据传输量大,存储成本高。由于数据所有权问题,甚至数据挖掘也不可行。本文提出了一种基于分布式异步连接的随机梯度下降算法(SGD),并对其进行了分布式实现,以解决多个分布式数据源问题。其中,算法的主要过程在分布式计算节点异步执行,模型可以在多个数据源中根据各自的计算环境进行局部训练,避免了数据集成和集中处理。并通过实验对该算法的可行性和性能进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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