… combine edge and cloud computing for IoT data analytics by taking advantage of edge nodes
… to locations, and feature learning is performed on the close by edge node. For comparison …

… of radio access networks and deep learning is still in the … deep learning in the development
of wireless communications. We highlight the intuitions and key technologies of deep learning…

… Recall that for the deep learning considered in this paper, we let c and er represent the cloud
node and the edge node that receives the input data, respectively. If we only allocate the L …

… In contrast to conventional distributed deep learning approaches, we have proposed a
DDTO algorithm with heterogeneous clouds, ie, the central cloud and the edge cloud, which …

… Consequently, this paper investigates merging cloud and edge computing for IoT … a deep
learning-based approach for data reduction on the edge with the machine learning on the cloud…

… reinforcement learning form is generated and we propose a distributed deep learning algorithm
to find the near-optimal computational offloading decisions in which a set of deep neural …

… the deep learning services for IoT in the edge computing … of scheduling IoT deep learning
tasks in edge computing. Then we … We first train the deep learning networks in the cloud server. …

… IoT services into geographically distributed physical edge/cloud nodes, and optimize traffic
… We argue that a deep learning method can be a novel alternative to deal with this kind of NP…

… , edge computing, into mobile deep learning applications. Comparing with cloud computing,
the … To this end, we note that there exists a layer-level partitioning strategy for deep neural …

… This article provides an overview of applications where deep learning is used at the network
edge. … of deep learning, a common approach is to leverage cloud computing. To use cloud …