  Machine learning (ML) and artificial intelligence (AI) have recently made a
significant impact on improving the operations of wireless networks and
establishing intelligence at the edge. In return, rare efforts were made to
explore how adapting, optimizing, and arranging wireless networks can
contribute to implementing ML/AI at the edge. This article aims to address this
void by setting a vision on how wireless networking researchers can leverage
their expertise to return the favor to edge learning. It will review the
enabling technologies, summarize the inaugural works on this path, and shed
light on different directions to establish a comprehensive framework for mobile
edge learning (MEL).
