  The next-generation of wireless networks will enable many machine learning
(ML) tools and applications to efficiently analyze various types of data
collected by edge devices for inference, autonomy, and decision making
purposes. However, due to resource constraints, delay limitations, and privacy
challenges, edge devices cannot offload their entire collected datasets to a
cloud server for centrally training their ML models or inference purposes. To
overcome these challenges, distributed learning and inference techniques have
been proposed as a means to enable edge devices to collaboratively train ML
models without raw data exchanges, thus reducing the communication overhead and
latency as well as improving data privacy. However, deploying distributed
learning over wireless networks faces several challenges including the
uncertain wireless environment, limited wireless resources (e.g., transmit
power and radio spectrum), and hardware resources. This paper provides a
comprehensive study of how distributed learning can be efficiently and
effectively deployed over wireless edge networks. We present a detailed
overview of several emerging distributed learning paradigms, including
federated learning, federated distillation, distributed inference, and
multi-agent reinforcement learning. For each learning framework, we first
introduce the motivation for deploying it over wireless networks. Then, we
present a detailed literature review on the use of communication techniques for
its efficient deployment. We then introduce an illustrative example to show how
to optimize wireless networks to improve its performance. Finally, we introduce
future research opportunities. In a nutshell, this paper provides a holistic
set of guidelines on how to deploy a broad range of distributed learning
frameworks over real-world wireless communication networks.
