Model Selection and Offloading for Digital Twin Network (DTN): Framework, Performance Metrics, and Algorithm Design

The technique of digital twin network (DTN) has been considered as a promising technique of network management and control to accommodate disruptive applications for the sixth generation communication (6G) systems. However, it is still challenging for DTN to provide ultimate experience for the emerging services and applications, due to the lack of effective DTN model management strategies. To solve this problem, the model selection and offloading for DTN is studied in this paper. First, a framework of cooperative model selection and offloading for DTN is designed, which can adapt with the limited computation capability of users, and improve the communication efficiency of model offloading. Second, the performance metrics are proposed to effectively evaluate the accuracy loss and the privacy leakage risk of model management for DTN, and tractable expressions are provided for our studied framework. Third, a joint optimization algorithm is designed for model management and transmit power allocation, which can efficiently reduce the accuracy loss and the privacy leakage risk with low processing latency. Finally, the experiment results are provided to show the effectiveness of our introduced performance metrics, and verify the performance gains of proposed optimization algorithm for our studied framework.