A Systematic Literature Review on Distributed Machine Learning in Edge Computing

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 7; p. 2665
• Main Authors: Filho, Carlos Poncinelli, Marques, Jr, Elias, Chang, Victor, Dos Santos, Leonardo, Bernardini, Flavia, Pires, Paulo F, Ochi, Luiz, Delicato, Flavia C
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.03.2022; MDPI
• Subjects: Algorithms; Artificial intelligence; Batch processing; Data processing; Deep learning; distributed; edge intelligence; Energy efficiency; Energy management; fog intelligence; Intelligence; Internet of Things; Literature reviews; Machine Learning; Review; Scheduling; Surveillance; Systematic review; distributed; edge intelligence; Internet of Things; machine learning; artificial intelligence; fog intelligence
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22072665

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.