1. INVITED: New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
- Author
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Radu Marculescu, Wei Chen, and Kartikeya Bhardwaj
- Subjects
Computer science ,business.industry ,Deep learning ,Inference ,Data security ,020206 networking & telecommunications ,02 engineering and technology ,Data science ,020202 computer hardware & architecture ,Software deployment ,0202 electrical engineering, electronic engineering, information engineering ,Enhanced Data Rates for GSM Evolution ,Artificial intelligence ,Internet of Things ,business - Abstract
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.
- Published
- 2020