Paper Info
Title
Edge Learning: The Enabling Technology for Distributed Big Data Analytics in the Edge
Abstract
AbstractMachine Learning (ML) has demonstrated great promise in various fields, e.g., self-driving, smart city, which are fundamentally altering the way individuals and organizations live, work, and interact. Traditional centralized learning frameworks require uploading all training data from different sources to a remote data server, which incurs significant communication overhead, service latency, and privacy issues.To further extend the frontiers of the learning paradigm, a new learning concept, namely, Edge Learning (EL) is emerging. It is complementary to the cloud-based methods for big data analytics by enabling distributed edge nodes to cooperatively training models and conduct inferences with their locally cached data. To explore the new characteristics and potential prospects of EL, we conduct a comprehensive survey of the recent research efforts on EL. Specifically, we first introduce the background and motivation. We then discuss the challenging issues in EL from the aspects of data, computation, and communication. Furthermore, we provide an overview of the enabling technologies for EL, including model training, inference, security guarantee, privacy protection, and incentive mechanism. Finally, we discuss future research opportunities on EL. We believe that this survey will provide a comprehensive overview of EL and stimulate fruitful future research in this field.
Year
DOI
Venue
2022
10.1145/3464419
ACM Computing Surveys
Keywords
DocType
Volume
Edge learning, machine learning, federated learning, edge computing, security and privacy
Journal
54
Issue
ISSN
Citations 
7
0360-0300
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Jie Zhang100.34
Zhihao Qu2425.45
Chenxi Chen300.34
Haozhao Wang441.77
Yufeng Zhan500.34
Baoliu Ye621232.11
Song Guo73431278.71