Saei-DT: Semi-Asynchronous Edge Intelligence for Industrial Digital Twin Networks in 6G

Promoted by integrating sensing, communication, and computing for edge intelligence in 6 G networks, the Digital Twin (DT), as a typical 6G application, has been widely employed for minimizing the disruption of Industrial Internet-of-Things (IIoT) operations and facilitating the cost-effective execution through conducting equivalent model training. Nevertheless, in complex and changeable industrial scenarios, the existing approaches of DT model construction and update within the Digital Twin Edge Network (DITEN), do not fully consider the heterogeneity in computing and communication capabilities, resulting in unbalanced model training cost and loss, which also cannot respond quickly to the environmental changes and ensure the data consistency of virtual-real space. To realize the collaborative and efficient utilization of heterogeneous computing and communication resources, and strengthen the balance of training cost and loss in the whole life cycle of DT model, an approach of using semi-asynchronous federated learning (FL) for DT construction and update, named Saei-DT, is proposed, which includes the three significant operations, e.g., terminal selection for model training, self-adaption of local training iterations, and semi-asynchronous global aggregation. Through conducting extensive experiments on the dataset of Industrial-Equipment contained in Kaggle, the experimental results demonstrate the efficiency of Saei-DT in both training accuracy and loss.