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Distributed Learning in the Nonconvex World: From batch data to streaming and beyond
IEEE Signal Processing Magazine ( IF 14.9 ) Pub Date : 2020-05-01 , DOI: 10.1109/msp.2020.2970170
Tsung-Hui Chang , Mingyi Hong , Hoi-To Wai , Xinwei Zhang , Songtao Lu

Distributed learning has become a critical enabler of the massively connected world that many people envision. This article discusses four key elements of scalable distributed processing and real-time intelligence: problems, data, communication, and computation. Our aim is to provide a unique perspective of how these elements should work together in an effective and coherent manner. In particular, we selectively review recent techniques developed for optimizing nonconvex models (i.e., problem classes) that process batch and streaming data (data types) across networks in a distributed manner (communication and computation paradigm). We describe the intuitions and connections behind a core set of popular distributed algorithms, emphasizing how to balance computation and communication costs. Practical issues and future research directions will also be discussed.

中文翻译:

非凸世界中的分布式学习:从批处理数据到流媒体等

分布式学习已成为许多人设想的大规模互联世界的关键推动因素。本文讨论了可扩展分布式处理和实时智能的四个关键要素:问题、数据、通信和计算。我们的目标是提供关于这些元素如何以有效和连贯的方式协同工作的独特视角。特别是,我们有选择地回顾了最近为优化非凸模型(即问题类)而开发的技术,这些模型以分布式方式(通信和计算范式)跨网络处理批处理和流数据(数据类型)。我们描述了一组核心流行分布式算法背后的直觉和联系,强调如何平衡计算和通信成本。
更新日期:2020-05-01
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