Energy-Efficient Edge Learning via Joint Data Deepening-and-Prefetching

arXiv:2402.11925v1 Announce Type: new
Abstract: The vision of pervasive artificial intelligence (AI) services can be realized by training an AI model on time using real-time data collected by internet of things (IoT) devices. To this end, IoT devices require offloading their data to an edge server in proximity. However, transmitting high-dimensional and voluminous data from energy-constrained IoT devices poses a significant challenge. To address this limitation, we propose a novel offloading architecture, called joint data deepening-and-prefetching (JD2P), which is feature-by-feature …

abstract ai model artificial artificial intelligence arxiv cs.ai cs.it cs.lg data devices edge energy intelligence internet internet of things iot math.it real-time server services time data training type via vision