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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)


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
Pages (from-to)1-36
JournalACM Computing Surveys
Issue number7
Publication statusPublished - Sept 2022


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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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