2020/01/14 by Tsung-Hui Chang, Mingyi Hong, Hoi-To Wai +2 · 10 citations
Computer Science · Mathematics · #Distributed Sensor Networks and Detection Algorithms #Privacy-Preserving Technologies in Data #Stochastic Gradient Optimization Techniques #cs.LG #math.OC #stat.ML
paper · pdf · doi:10.1109/msp.2020.2970170
Submitted to IEEE Signal Processing Magazine Special Issue on Distributed, Streaming Machine Learning; THC, MH, HTW contributed equally
arxiv created 2020/01/14 · arxiv updated 2020/06/24
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.