Related papers: Wireless for Machine Learning

Wireless for Machine Learning

URL: http://arxiv.org/abs/2008.13492v3
Date: Thu, 9 Jun 2022 15:56:18 GMT
Title: Wireless for Machine Learning
Authors: Henrik Hellstr\"om, Jos\'e Mairton B. da Silva Jr, Mohammad Mohammadi Amiri, Mingzhe Chen, Viktoria Fodor, H. Vincent Poor and Carlo Fischione
Abstract summary: We give an exhaustive review of the state-of-the-art wireless methods that are specifically designed to support machine learning services over distributed datasets. 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.
Score: 91.13476340719087
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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