Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-30T19:39:42.462Z Has data issue: false hasContentIssue false

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
Get access

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.

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • 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 Dropbox

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 Dropbox.

  • 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
×