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Machine learning (ML) and artificial intelligence (AI) have recently made asignificant impact on improving the operations of wireless networks andestablishing intelligence at the edge. In return, rare efforts were made toexplore how adapting, optimizing, and arranging wireless networks cancontribute to implementing ML/AI at the edge. This article aims to address thisvoid by setting a vision on how wireless networking researchers can leveragetheir expertise to return the favor to edge learning. It will review theenabling technologies, summarize the inaugural works on this path, and shedlight on different directions to establish a comprehensive framework for mobileedge learning (MEL).

Returning the Favor: What Wireless Networking Can Offer to AI and Edge Learning