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Edge learning for distributed big data analytics : theory, algorithms, and system design
\"\"Traditionally, to develop these intelligent services and applications, big data are stored and processed in a centralized model. However, with the proliferation of edge devices and edge data, traditional centralized learning frameworks are required to upload all training data from different sources to a remote data server, which incurs significant communication overhead, service latency, as well as security and privacy issues. Therefore, it is urgent to shift model training and inference from the cloud to the edge, which is the essential idea of edge learning. Edge Learning is a fusion of big data, edge computing, and machine learning, and it is an enabling technology for edge intelligence. This book presents the basic knowledge of training machine learning models, key challenges and issues in edge learning, and comprehensive techniques from three aspects, i.e., fundamental theory, edge learning algorithms, and system design issues in edge learning. We believe that this book will stimulate fruitful discussions, inspire further research ideas, and attract researchers and developers from both academia and industry in this field\"-- Provided by publisher.
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