Digital Twin Enabled Asynchronous SplitFed Learning in E-Healthcare Systems

The advancement of Industrial Internet of Things (IIoT) technology has resulted in the fourth industrial revolution, or Industry 4.0, enabling industries to enhance productivity. However, despite the benefits, there remain significant challenges, such as resource heterogeneity, communication efficiency, and data privacy, that limit the applications of IIoT in privacy-sensitive domains like healthcare. In order to protect data privacy, Federated Learning (FL) has been suggested as a solution, involving the sharing of model parameters rather than data itself. Current FL applications, however, still struggle with cost efficiency, especially when IIoT devices with heterogenous resources are involved. To address this, this paper proposes Digital Twin (DT) enabled Asynchronous SplitFed Learning (DT-ASFL) for classification tasks in the e-healthcare system over mobile networks. We first develop SplitFed Learning to introduce communication efficiency in the e-healthcare system, sending only extracted features during the learning process instead of the entire learning model. This enables resource-constrained devices to participate in the learning process by allowing the participants to train a partial learning model. DT is then employed to provide real-time statuses of IIoT devices deployed in the system, enabling asynchronous model updates in SplitFed Learning. The experimental results demonstrate the efficacy of DT-ASFL compared to the existing methods.