T-6: Wireless for Machine Learning

As data generation increasingly takes place on devices without a wired connection, machine learning over wireless networks becomes critical. Many studies have shown that traditional wireless protocols are highly inefficient or unsustainable to support distributed machine learning services. This is creating the need for new wireless communication methods that will be arguably included in 6G. In this tutorial, we plan to give a comprehensive review of the state-of-the-art wireless methods that are specifically designed to support distributed machine learning services. Namely, over-the-air computation and radio resource allocation optimized for machine learning. In the over-the-air approach, multiple devices communicate simultaneously over the same time slot and frequency band to exploit the superposition property of wireless channels for gradient averaging over-the-air. In radio resource allocation optimized for machine learning, active learning metrics allow data evaluation to greatly optimize the assignment of radio resources. This tutorial introduces these methods, reviews the most important works, and highlights crucial open problems.