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Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in internet of vehicles
2020
Intelligent and Converged Networks
Nowadays, Edge Information System (EIS) has received a lot of attentions. In EIS, Distributed Machine Learning (DML), which requires fewer computing resources, can implement many artificial intelligent applications efficiently. However, due to the dynamical network topology and the fluctuating transmission quality at the edge, work node selection affects the performance of DML a lot. In this paper, we focus on the Internet of Vehicles (IoV), one of the typical scenarios of EIS, and consider the
doi:10.23919/icn.2020.0015
fatcat:tpf2qbs64nhszowhxlamlaff7a