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

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. We are living in a highly connected world, and it will become exponentially more connected in a decade. These devices collect a huge amount of real-time data, perform complex computational tasks, and provide vital services which significantly improve our lives and enrich our collective productivity. THC, MH, HTW are ordered alphabetically, and contributed equally. MH is the corresponding author. THC is with the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China. MH and XZ are with the ECE Department, University of Minnesota, MN, USA. HTW is with the Department of SEEM, The Chinese University of Hong Kong, Hong Kong SAR, China. SL is with IBM Research AI, IBM Thomas J. Watson Research Center Y orktown Heights, New Y ork 10598, USA.