Edge Learning: The Enabling Technology for Distributed Big Data Analytics in the Edge

Jie Zhang, Zhihao Qu*, Chenxi Chen, Haozhao Wang, Yufeng Zhan, Baoliu Ye, Song Guo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Machine 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.

Original languageEnglish
Article number151
JournalACM Computing Surveys
Volume54
Issue number7
DOIs
Publication statusPublished - Sept 2022
Externally publishedYes

Keywords

  • Edge learning
  • edge computing
  • federated learning
  • machine learning
  • security and privacy

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