Statistical inference and machine-learning algorithms have traditionally been developed for data available at a single location. Unlike this centralized setting, modern data sets are increasingly being distributed across multiple physical entities (sensors, devices, machines, data centers, and so on) for a multitude of reasons that range from storage, memory, and computational constraints to privacy concerns and engineering needs. This has necessitated the development of inference and learning algorithms capable of operating on noncolocated data. For this article, we divide such algorithms into two broad categories, namely, distributed algorithms and decentralized algorithms (see "Is It Distributed or Is It Decentralized?").

中文翻译：

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对手弹性分布式和分散统计推理和机器学习：拜占庭威胁模型下的最新进展概述

传统上，统计推断和机器学习算法是针对单个位置可用的数据开发的。与这种集中式设置不同，现代数据集越来越多地分布在多个物理实体（传感器、设备、机器、数据中心等）上，原因多种多样，从存储、内存和计算限制到隐私问题和工程需要。这需要开发能够对非协同定位数据进行操作的推理和学习算法。在本文中，我们将此类算法分为两大类，即分布式算法和去中心化算法（参见“是分布式还是去中心化？”）。