A Systematic Literature Review on Distributed Machine Learning in Edge Computing

Sensors

Victor Chang¹  Flavia Bernardini²  Flavia C. Delicato²  Leonardo dos Santos²  Luiz Ochi²  Elias Marques²  Paulo F. Pires²  Carlos Poncinelli Filho² 

[1] Department of Operations and Information Management, Aston Business School, Aston University, Birmingham B4 7ET, UK;Institute of Computing, Universidade Federal Fluminense, Av. Gal. Milton Tavares de Souza, São Domingos, Niterói 24210-310, RJ, Brazil;

关键词: machine learning;    artificial intelligence;    distributed;    edge intelligence;    fog intelligence;    Internet of Things;   

DOI  :  10.3390/s22072665

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown