Wireless for Machine Learning: A Survey

As data generation increasingly takes place on devices withouta wired connection, Machine Learning (ML) related traffic willbe ubiquitous in wireless networks. Many studies have shownthat traditional wireless protocols are highly inefficient or unsustainableto support ML, which creates the need for new wirelesscommunication methods. In this monograph, we give a comprehensivereview of the state-of-the-art wireless methods that arespecifically designed to support ML services over distributeddatasets. Currently, there are two clear themes within the literature,analog over-the-air computation and digital radio resourcemanagement optimized for ML. This survey gives an introductionto these methods, reviews the most important works, highlightsopen problems, and discusses application scenarios.QC 20220610