Edge Learning for Large-Scale Internet of Things With Task-Oriented Efficient Communication

Haihui Xie, Minghua Xia, Peiran Wu, Shuai Wang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In Internet of Things (IoT) networks, edge learning for data-driven tasks provides intelligent applications and services. As the network size becomes large, different users may generate distinct datasets. Thus, to suit multiple edge learning tasks for large-scale IoT networks, this paper considers efficient communication under a task-oriented principle by using the collaborative design of wireless resource allocation and edge learning error prediction. In particular, we start with multi-user scheduling to alleviate co-channel interference in dense networks. Then, we perform optimal power allocation in parallel for different learning tasks. Thanks to the high parallelization of the designed algorithm, extensive experimental results corroborate that the multi-user scheduling and task-oriented power allocation improve the performance of distinct edge learning tasks efficiently compared with the state-of-the-art benchmark algorithms.

Original languageEnglish (US)
Pages (from-to)9517-9532
Number of pages16
JournalIEEE Transactions on Wireless Communications
Volume22
Issue number12
DOIs
StatePublished - Dec 1 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Electrical and Electronic Engineering
  • Computer Science Applications

Keywords

  • Edge learning
  • internet of things
  • multi-user scheduling
  • parallel computing

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