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4 - Communication-Efficient Edge Learning

Published online by Cambridge University Press:  14 January 2022

Song Guo
Affiliation:
The Hong Kong Polytechnic University
Zhihao Qu
Affiliation:
The Hong Kong Polytechnic University
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Summary

Edge learning has enabled the training of large-scale machine learning models on a big dataset by implementing data parallelism in multiple nodes. However, the iterative interaction generated by multiple learning nodes together with the considerable quantity of communication data on each interaction yields huge communication overhead, which greatly hinders the scalability of Edge Learning. In this chapter, we introduce the mainstream approaches to achieve communication efficiency of edge training, including compressing communication data, reducing the synchronous frequency, overlapping computation and communication, and optimizing the transmission network. Specifically, we propose two hybrid mechanisms for communication-efficient Edge Learning. The first one is QOSP that integrates gradient quantization for communication compression and overlap synchronization parallel for simultaneous computation and communication. The second mechanism improves communication efficiency during the aggregation of client-side updates by quantizing the gradients and exploiting the inherent superposition of radio frequency signals. Finally, we discuss the future directions of communication-efficient edge learning.

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Chapter
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Edge Learning for Distributed Big Data Analytics
Theory, Algorithms, and System Design
, pp. 42 - 72
Publisher: Cambridge University Press
Print publication year: 2022

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  • Communication-Efficient Edge Learning
  • Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
  • Book: Edge Learning for Distributed Big Data Analytics
  • Online publication: 14 January 2022
  • Chapter DOI: https://doi.org/10.1017/9781108955959.006
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  • Communication-Efficient Edge Learning
  • Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
  • Book: Edge Learning for Distributed Big Data Analytics
  • Online publication: 14 January 2022
  • Chapter DOI: https://doi.org/10.1017/9781108955959.006
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Communication-Efficient Edge Learning
  • Song Guo, The Hong Kong Polytechnic University, Zhihao Qu, The Hong Kong Polytechnic University
  • Book: Edge Learning for Distributed Big Data Analytics
  • Online publication: 14 January 2022
  • Chapter DOI: https://doi.org/10.1017/9781108955959.006
Available formats
×