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Published online by Cambridge University Press: 14 January 2022
In a cloud-edge environment, data are generated by different types of devices, and these devices have various computation capabilities and storage sizes. It is unrealistic to execute all the tasks in the cloud, instead, putting some work into edge servers that are close to end-users would be more reasonable. Edge Learning is a powerful paradigm for big data analytics in the cloud-edge environment. Edge Learning exploits pervasive data generated not only by user devices but also by other sensing devices and those stored in the cloud/edge servers (e.g., data from social networks). Moreover, EL leverages various computing entities (all the devices with computing capabilities ranging from cloud, edge servers, to various edge devices) in an efficient, reliable, and robust manner.
In this chapter, we first introduce the deep learning models that are widely used in Edge Learning. Then, we introduce the basic machine learning algorithms, architectures, and synchronization mode for Edge Learning.
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