EDeLeaR: Edge-based Deep Learning with Resource Awareness for Efficient Model Training and Inference for IoT and Edge Devices
Keywords:
Edge Computing, Deep learning, IoT, Network Security, Resource-awareness, Model Compression & Collaborative learningAbstract
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.
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