Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond
It addresses scalable distributed processing and real-time intelligence for applications in connected systems, but it is incremental as it reviews existing methods.
This paper provides a selective review of distributed learning techniques for optimizing non-convex models with batch and streaming data, focusing on how to trade off computation and communication costs in networks.
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.