New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
It addresses the problem of scaling deep learning for IoT applications, but it is incremental as it synthesizes existing directions without presenting new results.
The paper tackles the challenges of adopting deep learning at the IoT edge, including hardware constraints, data privacy, and lack of network-aware algorithms, and proposes a unified view with research directions like federated learning and communication-aware inference to enable edge intelligence.
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.