Cloud-Edge Intelligent Collaborative Computing Model Based on Transfer Learning in IoT

The deep neural network is a reliable technical support for cloud com-puting and edge computing.It has excellent nonlinear approximation and gener-alization capabilities,making it suitable for classifying and predicting Internet of Things data in cloud computing and edge computingfields.However,the increas-ing size of neural networks poses a challenge for their deployment on devices with limited computing and storage resources.Traditional cloud computing ser-vices also suffer from high latency,which hinders real-time tasks.To address these challenges,this paper proposes a cloud-side cooperation model for deep learning based on migration learning technology.This model used migration learning tech-nology to reduce the size of deep neural networks.Specifically,it deployed the deep neural network model(CDLM)in the cloud and the shallow neural network model(EDLM)at the edge.CDLM is used to help train EDLM and improve its performance,enabling it to run independently on edge devices with high accu-racy and respond to real-time tasks.This approach reduced the amount of user data transmitted to the cloud,alleviated bandwidth pressure,and protected user privacy.Experimental results show that the proposed model improved the accu-racy of EDLM by 19.58% compared with traditional neural network models.Thesefindings provide a theoretical and experimental foundation for the study of cloud-edge collaborative models.