DistributedML 2020

Distributed Machine Learning

Lessons learned from industrial research at the intersection of ML and Networks

Ilias Leontiadis

In recent years, there has been a constant growth of research at the intersection of machine learning and networks. This interaction has been beneficial for both parties; Machine learning has been applied to monitor and improve network QoE while networks have been optimised to support distributed training and inference. In this talk we will thus describe industrial research that covers both directions. Firstly, we will discuss how machine learning has been used to monitor the customer QoE in large-scale cellular networks that serve millions of devices and will examine how distributed systems play a pivot role in training such systems. Secondly, we will demonstrate how cellular networks can assist distributed inference and training and look into future directions.

Overview Program