Analysis of Techniques for Mapping Convolutional Neural Networks onto Cloud Edge Architectures Using SplitFed Learning Method

Martino, Beniamino Di; Graziano, Mariangela; Cante, Luigi Colucci; Cascone, Datiana

doi:10.1007/978-3-030-99619-2_16

Cited by 5 publications

(1 citation statement)

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“…Some researchers have used the splitting mechanism for the machine learning models such as CNN so that some of the layers can run on the cloud and some on the edge servers. The layers that need more computational power can be executed on the cloud such as feature extraction and training while the layers that do not need much compute resources such as prediction and data collection might be run on the edge [20][21][22][23][24][25]. However, this kind of distribution might be difficult and will vary from application to application.…”

Section: Applications Of Edge Computing In Resource Integration Of Te...mentioning

confidence: 99%

A convolutional neural network based online teaching method using edge-cloud computing platform

Liu

2023

J Cloud Comp

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Teaching has become a complex essential tool for students’ abilities, due to their different levels of learning and understanding. In the traditional offline teaching methods, dance teachers lack a target for students ‘classroom teaching. Furthermore, teachers have limited time, so they cannot take full care of each student’s learning needs according to their understanding and learning ability, which leads to the polarization of the learning effect. Because of this, this paper proposes an online teaching method based on Artificial Intelligence and edge calculation. In the first phase, standard teaching and student-recorded dance learning videos are conducted through the key frames extraction through a deep convolutional neural network. In the second phase, the extracted key frame images were then extracted for human key points using grid coding, and the fully convolutional neural network was used to predict the human posture. The guidance vector is used to correct the dance movements to achieve the purpose of online learning. The CNN model is distributed into two parts so that the training occurs at the cloud and prediction happens at the edge server. Moreover, the questionnaire was used to obtain the students’ learning status, understand their difficulties in dance learning, and record the corresponding dance teaching videos to make up for their weak links. Finally, the edge-cloud computing platform is used to help the training model learn quickly form vast amount of collected data. Our experiments show that the cloud-edge platform helps to support new teaching forms, enhance the platform’s overall application performance and intelligence level, and improve the online learning experience. The application of this paper can help dance students to achieve efficient learning.

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