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In this paper, we first highlight three major challenges to large-scaleadoption 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-awaredeep learning algorithms for distributed inference across multiple IoT devices.We then provide a unified view targeting three research directions thatnaturally emerge from the above challenges: (1) Federated learning for trainingdeep networks, (2) Data-independent deployment of learning algorithms, and (3)Communication-aware distributed inference. We believe that the above researchdirections need a network-centric approach to enable the edge intelligence and,therefore, fully exploit the true potential of IoT.

New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design