  Bringing the success of modern machine learning (ML) techniques to mobile
devices can enable many new services and businesses, but also poses significant
technical and research challenges. Two factors that are critical for the
success of ML algorithms are massive amounts of data and processing power, both
of which are plentiful, yet highly distributed at the network edge. Moreover,
edge devices are connected through bandwidth- and power-limited wireless links
that suffer from noise, time-variations, and interference. Information and
coding theory have laid the foundations of reliable and efficient
communications in the presence of channel imperfections, whose application in
modern wireless networks have been a tremendous success. However, there is a
clear disconnect between the current coding and communication schemes, and the
ML algorithms deployed at the network edge. In this paper, we challenge the
current approach that treats these problems separately, and argue for a joint
communication and learning paradigm for both the training and inference stages
of edge learning.
