  In 5G and Beyond networks, Artificial Intelligence applications are expected
to be increasingly ubiquitous. This necessitates a paradigm shift from the
current cloud-centric model training approach to the Edge Computing based
collaborative learning scheme known as edge learning, in which model training
is executed at the edge of the network. In this article, we first introduce the
principles and technologies of collaborative edge learning. Then, we establish
that a successful, scalable implementation of edge learning requires the
communication, caching, computation, and learning resources (3C-L) of end
devices and edge servers to be leveraged jointly in an efficient manner.
However, users may not consent to contribute their resources without receiving
adequate compensation. In consideration of the heterogeneity of edge nodes,
e.g., in terms of available computation resources, we discuss the challenges of
incentive mechanism design to facilitate resource sharing for edge learning.
Furthermore, we present a case study involving optimal auction design using
Deep Learning to price fresh data contributed for edge learning. The
performance evaluation shows the revenue maximizing properties of our proposed
auction over the benchmark schemes.
