  In this paper, we first highlight three major challenges to large-scale
adoption of deep learning at the edge: (i) Hardware-constrained IoT devices,
(ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware
deep learning algorithms for distributed inference across multiple IoT devices.
We then provide a unified view targeting three research directions that
naturally emerge from the above challenges: (1) Federated learning for training
deep networks, (2) Data-independent deployment of learning algorithms, and (3)
Communication-aware distributed inference. We believe that the above research
directions need a network-centric approach to enable the edge intelligence and,
therefore, fully exploit the true potential of IoT.
