Polymorphic Learning of Heterogeneous Resources in Digital Twin Networks
Chengzhuo Han, Tingting Yang, Xianbin Li, Kai You Wang, Hailong Feng · 2022
The emergence of digital twin technology is expected to reduce the significant cost of traditional physical debugging. Moreover, it can also fully combine IoT real-time data, large data analysis, and simulation, to make the best decisions. In the digital twin network, the data distribution and computing power of different device nodes shows great heterogeneity, but the internal node data are independent and identically distributed. Therefore, our learning algorithm should be able to learn not only the network commonality, but also the node specificity. In order to provide secure and personalised services for different device nodes, a polymorphic learning (PL) framework is proposed in this paper. PL divides the classical neural network model into a homomorphic model and a polymorphic model to realise flexible network control. Finally, the advantages of the PL algorithm in the collaborative optimisation of different nodes are proven through simulation experiments, and the algorithm running process is simulated through a classic case, with the final results proving the superiority of the algorithm.