  To process and transfer large amounts of data in emerging wireless services,
it has become increasingly appealing to exploit distributed data communication
and learning. Specifically, edge learning (EL) enables local model training on
geographically disperse edge nodes and minimizes the need for frequent data
exchange. However, the current design of separating EL deployment and
communication optimization does not yet reap the promised benefits of
distributed signal processing, and sometimes suffers from excessive signalling
overhead, long processing delay, and unstable learning convergence. In this
paper, we provide an overview on practical distributed EL techniques and their
interplay with advanced communication optimization designs. In particular,
typical performance metrics for dual-functional learning and communication
networks are discussed. Also, recent achievements of enabling techniques for
the dual-functional design are surveyed with exemplifications from the mutual
perspectives of "communications for learning" and "learning for
communications." The application of EL techniques within a variety of future
communication systems are also envisioned for beyond 5G (B5G) wireless
networks. For the application in goal-oriented semantic communication, we
present a first mathematical model of the goal-oriented source entropy as an
optimization problem. In addition, from the viewpoint of information theory, we
identify fundamental open problems of characterizing rate regions for
communication networks supporting distributed learning-and-computing tasks. We
also present technical challenges as well as emerging application opportunities
in this field, with the aim of inspiring future research and promoting
widespread developments of EL in B5G.
