A Systematic Literature Review on Distributed Machine Learning in Edge Computing

Author:

Filho Carlos PoncinelliORCID,Marques EliasORCID,Chang VictorORCID,dos Santos Leonardo,Bernardini FlaviaORCID,Pires Paulo F.ORCID,Ochi LuizORCID,Delicato Flavia C.ORCID

Abstract

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.

Funder

São Paulo Research Foundation

Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

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