… tasks on end-edge-cloud architectures [15], [16]. In this work, we adapt such hybrid learning
strategy for orchestrating deep learning tasks on distributed end-edge-cloud architectures. …

… the learning process of DNNs on the Mobile-Edge-Cloud Computing (MECC) paradigm. In
this paper, we propose HierTrain, a hierarchical edge AI learning … We develop a novel hybrid …

… Hybrid Learning, the first HDC based hybrid learning algorithm for orchestrating deep
learning tasks in an end-edge-cloud … its computational efficiency and effective learning quality. …

… cloud edge-based two-level hybrid scheduling learning model proposed in this paper has
a three-tier architecture: the first layer is the cloud … The second layer is the edge scheduling …

… edge and the highperformance cloud to further improve the overall DNN inference performance,
we propose to a hybrid … the device, the edge, and the cloud. Instead of partitioning the …

… hybrid cloud and edge control strategy for BESS and HVAC based on deep reinforcement
learning … , we present a hybrid cloud and edge control strategy for DR using DRL and transfer …

… problem in edge-cloud hybrid environment with … edge servers. Through theoretical formulation
and analysis, it is proved to make the most use of the characteristics in edge-cloud hybrid …

… This paper presents a hybrid Random Forest Deep learning (HRFDL) classifier for COVID-19
and pneumonia disease diagnosis in IoT-based edge devices. The Random Forest …

… ABSTRACT Cloud-edge computing is a hybrid model of computing where resources and …
cloud layer and small-scale and short-term data as edge layer. The main challenge of the cloud …

… According to scheduling algorithm, we design the learning system for a given edge-cloud
computing. We present the system model in multi-client AMI data stream environment. …