Management of Digital Twin-Driven IoT Using Federated Learning

Internet of Things (IoT), Digital Twin (DT), and Federated Learning (FL) are redefining the future vision of globalization. While IoT is about sensing data from physical devices, DTs reflect their digital representation and enable optimized decision-making by tightly integrating Artificial Intelligence (AI). Although swiftly growing, DTs are raising new challenges in privacy concerns, which are nowadays addressed by FL. However, the limited IoT resources, the communication overhead, and the lack of trust among clients are major obstacles that hinder the effectiveness of learning systems. In this paper, we design a new IoT-based architecture empowered by DT to improve the efficiencies of limited-resources devices. On top of this architecture, we leverage FL to construct the DT models. We further propose CISCO-FL, a Clustered FL with Intelligent Selection and Computation Offloading. Particularly, we study the computing resources of the clients and the quality of their models, and we embed in the proposed approach an intelligent offloading model, where the clients with high computational resources can assist and optimize the model of those struggling with limited resources. As such, both communication cost and computation resources are reduced and optimized. Finally, thorough experimental results are presented to support our findings and validate our model.