A Comprehensive Survey on Training Acceleration for Large Machine Learning Models in IoT

Haozhao Wang, Zhihao Qu, Qihua Zhou, Haobo Zhang, Boyuan Luo, Wenchao Xu, Song Guo, Ruixuan Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)


The ever-growing artificial intelligence (AI) applications have greatly reshaped our world in many areas, e.g., smart home, computer vision, natural language processing, etc. Behind these applications are usually machine learning (ML) models with extremely large size, which require huge data sets for accurate training to mine the value contained in the big data. Large ML models, however, can consume tremendous computing resources to achieve decent performance and thus, it is difficult to train them in resource-constrained Internet of Things (IoT) environments, which would prevent further development and application of AI techniques in the future. To deal with such challenges, there are many efforts on accelerating the training process for large ML models in IoT. In this article, we provide a comprehensive review on the recent advances toward reducing the computing cost during the training stage while maintaining comparable model accuracy. Specifically, the optimization algorithms that aim to improve the convergence rate are emphasized over various distributed learning architectures that exploit ubiquitous computing resources. Then, the article elaborates the computation hardware acceleration and communication optimization for collaborative training among multiple learning entities. Finally, the remaining challenges, future opportunities, and possible directions are discussed.

Original languageEnglish
Pages (from-to)939-963
Number of pages25
JournalIEEE Internet of Things Journal
Issue number2
Publication statusPublished - 15 Jan 2022


  • Distributed machine learning (ML)
  • hardware-aided acceleration
  • large model training
  • training acceleration

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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