EDeLeaR: Edge-based Deep Learning with Resource Awareness for Efficient Model Training and Inference for IoT and Edge Devices

Mansoor Farooq¹ , Mubashir Hassan Khan²

Section:Research Paper, Product Type: Journal
Vol.12 , Issue.1 , pp.1-8, Feb-2024

Online published on Feb 28, 2024

Copyright © Mansoor Farooq, Mubashir Hassan Khan . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Deep learning has emerged as a powerful technique for processing and extracting insights from complex data. However, the resource-constrained nature of edge devices poses significant challenges to the deployment of deep learning models at the network edge. This research proposes a novel algorithm called EDeLeaR, which stands for Edge-based Deep Learning with Resource-awareness, to enable efficient model training and inference in edge computing environments. EDeLeaR leverages adaptive resource allocation and optimization techniques to maximize the utilization of limited computational resources while preserving model accuracy and minimizing latency. This paper presents the design, implementation, and evaluation of EDeLeaR, showcasing its effectiveness through comprehensive experiments on real-world edge devices.

Key-Words / Index Term :
Edge Computing, Deep learning, IoT, Network Security, Resource-awareness, Model Compression & Collaborative learning

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