再论大规模分布式机器学习
Revisiting Large Scale Distributed Machine Learning
摘要：
如今，随着配备各种传感器的智能手机和其他便携式小工具的普及，数据无处不在，机器学习的重点已经从能够从小训练样本中进行推断转移到处理大规模高维数据。在个人医疗保健应用等领域，分布式机器学习是一个很有前途的研究方向，既可以扩展学习算法，也可以处理在不同位置产生的数据。本报告全面概述了分布式机器学习的最新算法，包括监督学习和无监督学习，从简单的线性逻辑回归到图形模型和聚类。我们为大多数类别提出了未来的发展方向，具体到潜在的个人医疗保健应用。考虑到这一点，报告重点关注在严格的客户端-服务器架构模型的特定情况下如何确保安全性和低通信开销。作为特定的方向，我们提供了一个详尽的介绍经验聚类算法，k-窗口，并提出了一个异步分布式机器学习算法，将规模以及计算成本低，易于实现。
英文摘要：
Nowadays, with the widespread of smartphones and other portable gadgets equipped with a variety of sensors, data is ubiquitous available and the focus of machine learning has shifted from being able to infer from small training samples to dealing with large scale high-dimensional data. In domains such as personal healthcare applications, which motivates this survey, distributed machine learning is a promising line of research, both for scaling up learning algorithms, but mostly for dealing with data which is inherently produced at different locations. This report offers a thorough overview of and state-of-the-art algorithms for distributed machine learning, for both supervised and unsupervised learning, ranging from simple linear logistic regression to graphical models and clustering. We propose future directions for most categories, specific to the potential personal healthcare applications. With this in mind, the report focuses on how security and low communication overhead can be assured in the specific case of a strictly client-server architectural model. As particular directions we provides an exhaustive presentation of an empirical clustering algorithm, k-windows, and proposed an asynchronous distributed machine learning algorithm that would scale well and also would be computationally cheap and easy to implement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
