A Systematic Literature Review on Distributed Machine Learning in Edge Computing


Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies.

Publication DOI: https://doi.org/10.3390/s22072665
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). Funding Information: This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001, also by Brazilian funding agencies FAPESP (grant number 2015/24144-7), FAPERJ and CNPq. Prof. Chang’s work is partly supported by VC Research (VCR0000170).
Uncontrolled Keywords: artificial intelligence,distributed,edge intelligence,fog intelligence,Internet of Things,machine learning,Analytical Chemistry,Information Systems,Atomic and Molecular Physics, and Optics,Biochemistry,Instrumentation,Electrical and Electronic Engineering
Publication ISSN: 1424-8220
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.mdp ... -8220/22/7/2665 (Publisher URL)
PURE Output Type: Review article
Published Date: 2022-03-30
Accepted Date: 2022-03-23
Authors: Filho, Carlos Poncinelli
Marques, Elias
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Dos Santos, Leonardo
Bernardini, Flavia
Pires, Paulo F.
Ochi, Luiz
Delicato, Flavia C.



Version: Published Version

License: Creative Commons Attribution

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