Journal
SENSORS
Volume 22, Issue 7, Pages -Publisher
MDPI
DOI: 10.3390/s22072665
Keywords
machine learning; artificial intelligence; distributed; edge intelligence; fog intelligence; Internet of Things
Funding
- CoordenacAo de Aperfeicoamento de Pessoal de Nivel Superior-Brasil (CAPES) [001]
- FAPESP [2015/24144-7]
- FAPERJ
- CNPq
- VC Research [VCR0000170]
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This paper investigates the challenges of running machine learning and deep learning algorithms on edge devices in a distributed manner. It focuses on how techniques are adapted or designed for execution on restricted devices, discussing techniques in caching, training, inference, and offloading processes, as well as exploring the benefits and drawbacks of these strategies.
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.
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