论文标题
分布式深度学习的新方向：将网络带到物联网设计的最前沿
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
论文作者
论文摘要
在本文中，我们首先强调了在边缘大规模采用深度学习方面的三个主要挑战：（i）由硬件受限的物联网设备，（ii）物联网时代的数据安全性和隐私性，以及（iii）缺乏网络意识到的深度学习算法，用于在多个物联网设备上分布式推理。然后，我们提供了一个统一的视图，针对以上挑战自然出现的三个研究方向：（1）用于培训深网络的联合学习，（2）与数据无关学习算法的部署，以及（3）通信意识到分布式推荐。我们认为，上述研究方向需要一种以网络为中心的方法来实现边缘智能，因此充分利用了物联网的真正潜力。
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.
