The emergence of edge computing provides a new solution to big data processing in the Internet of Things (IoT) environment. By combining edge computing with deep neural network, it can make better use of the advantages of multi-layer architecture of the network. However, the current task offloading and scheduling frameworks for edge computing are not well applicable to neural network training tasks. In this paper, we propose a task model offloading algorithm by considering how to optimally deploy neural network model into the edge nodes. An adaptive task scheduling algorithm is also designed to adaptively optimize the task assignment by using the improved ant colony algorithm. Based on them, a collaborative cloud-edge computing framework is proposed, which can be used in the distributed neural network. Moreover, this framework sets up some mechanisms so that the cloud can collaborate with edge computing in the work. The simulation results show that the framework can reduce time delay and energy consumption, and improve task accuracy.

边缘计算的出现为物联网（IoT）环境中的大数据处理提供了新的解决方案。通过将边缘计算与深度神经网络相结合，可以更好地利用网络多层架构的优势。但是，当前用于边缘计算的任务卸载和调度框架不适用于神经网络训练任务。在本文中，我们考虑了如何将神经网络模型最佳地部署到边缘节点中，提出了一种任务模型卸载算法。还设计了一种自适应任务调度算法，以使用改进的蚁群算法自适应地优化任务分配。在此基础上，提出了一种可在分布式神经网络中使用的协同云边缘计算框架。此外，该框架建立了一些机制，以便云可以在工作中与边缘计算进行协作。仿真结果表明，该框架可以减少时间延迟和能耗，提高任务准确性。