Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond

• URL: http://arxiv.org/abs/2001.04786v1
• Date: Tue, 14 Jan 2020 14:11:32 GMT
• Title: Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond
• Authors: Tsung-Hui Chang, Mingyi Hong, Hoi-To Wai, Xinwei Zhang, and Songtao Lu
• Abstract summary: Distributed learning has become a critical direction of the massively connected world envisioned by many. This article discusses four key elements of scalable distributed processing and real-time data computation problems. Practical issues and future research will also be discussed.
• Score: 73.03743482037378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed learning has become a critical enabler of the massively connected world envisioned by many. 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 fresh and unique perspective about how these elements should work together in an effective and coherent manner. In particular, we {provide a selective review} about the recent techniques developed for optimizing non-convex models (i.e., problem classes), processing batch and streaming data (i.e., data types), over the networks in a distributed manner (i.e., communication and computation paradigm). We describe the intuitions and connections behind a core set of popular distributed algorithms, emphasizing how to trade off between computation and communication costs. Practical issues and future research directions will also be discussed.

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