The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to develop novel algorithms. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimation and inference have been proposed. They were developed to deal with large-scale statistical optimization problems. This paper aims to provide a comprehensive review for related literature. It includes parametric models, nonparametric models, and other frequently used models. Their key ideas and theoretical properties are summarized. The trade-off between communication cost and estimate precision together with other concerns are discussed.

翻译：随着各领域海量数据的迅速涌现，传统统计方法面临着严峻挑战。与此同时，这为研究者开发新型算法提供了机遇。受分治思想启发，各类面向统计估计与推断的分布式框架相继提出，旨在解决大规模统计优化问题。本文旨在对相关文献进行全面综述，涵盖参数模型、非参数模型及其他常用模型，总结其核心思想与理论性质，并探讨通信成本与估计精度之间的权衡关系及其他关注要点。