Khan Muhammad, Javier Del Ser, Naercio Magaia, et al., “Communication Technologies for Edge Learning and Inference: A Novel Framework, Open Issues, and Perspectives”, in IEEE Network Magazine, 2022.

With the continuous advancement of smart devices and their demand for data, the complex computation that was previously exclusive to the cloud server is now moving towards the edge of the network. Due to numerous reasons (e.g., applications demanding low latencies and data privacy), data-based computation has been brought closer to the originating source, forging the Edge Computing paradigm. Together with Machine Learning, Edge Computing has turned into a powerful local decision-making tool, thus fostering the advent of Edge Learning. The latter, however, has become delay-sensitive as well as resource-thirsty in terms of hardware and networking. New methods have been developed to solve or, at least, minimize these issues, as proposed in this research. In this study, we first investigate representative communication methods for edge learning and inference (ELI), focusing on data compression, latency, and resource management. Next, we propose an ELI-based video data prioritization framework which only considers the data having events and hence significantly reduces the transmission and storage resources when implemented in surveillance networks. Furthermore, in this overview, we critically examine various communication aspects related to Edge Learning by analyzing their issues and highlighting their advantages and disadvantages. Finally, we discuss challenges and present issues that are yet to be overcome.