  Machine learning (ML) is a promising enabler for the fifth generation (5G)
communication systems and beyond. By imbuing intelligence into the network
edge, edge nodes can proactively carry out decision-making, and thereby react
to local environmental changes and disturbances while experiencing zero
communication latency. To achieve this goal, it is essential to cater for high
ML inference accuracy at scale under time-varying channel and network dynamics,
by continuously exchanging fresh data and ML model updates in a distributed
way. Taming this new kind of data traffic boils down to improving the
communication efficiency of distributed learning by optimizing communication
payload types, transmission techniques, and scheduling, as well as ML
architectures, algorithms, and data processing methods. To this end, this
article aims to provide a holistic overview of relevant communication and ML
principles, and thereby present communication-efficient and distributed
learning frameworks with selected use cases.
