  As data generation increasingly takes place on devices without a wired
connection, machine learning (ML) related traffic will be ubiquitous in
wireless networks. Many studies have shown that traditional wireless protocols
are highly inefficient or unsustainable to support ML, which creates the need
for new wireless communication methods. In this survey, we give an exhaustive
review of the state-of-the-art wireless methods that are specifically designed
to support ML services over distributed datasets. Currently, there are two
clear themes within the literature, analog over-the-air computation and digital
radio resource management optimized for ML. This survey gives a comprehensive
introduction to these methods, reviews the most important works, highlights
open problems, and discusses application scenarios.
