Cooperative Knowledge-Distillation-Based Tiny DNN for UAV-Assisted Mobile-Edge Network

Unmanned aerial vehicles (UAVs) can be deployed in the areas where traditional network infrastructure is insufficient or absent because of flexibility and collaboration. The deployment of edge intelligence on UAVs in UAV-assisted mobile-edge networks significantly enhance data processing efficiency, which is a critical factor for applications requiring delay-sensitive data processing in the areas mentioned above. However, the limited energy capacity poses a challenge when running complex deployment algorithms. Therefore, lightweight network model is essential for deployment algorithms, as it significantly reduces energy and time consumption. In this article, we propose a cooperative framework-based knowledge distillation to compressed deep neural network (DNN). The subnetworks collaborate to train interactive node parameters, resulting in the optimal evolution of the student network. Then, we introduce a novel result-driven model training approach for simulation data sets. To further enhance efficiency and significantly reduce overall latency, we meticulously refine the internal architecture of the knowledge distillation algorithm. We incorporate a collaborative evolution mechanism into the core of the algorithm, utilizing multinetwork and subnetwork learning to facilitate knowledge transfer, and incorporate some optimization mechanisms into the framework. Finally, we perform a series of experiments to acquiredata sets and conduct algorithm simulation analysis to evaluate the proposed method. The results demonstrate that our work achieves good research results.