Incentive-Based Federated Learning for Digital-Twin-Driven Industrial Mobile Crowdsensing
Beibei Li, Yaxin Shi, Qinglei Kong, Qingyun Du, Rongxing Lu
Abstract
Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways, such as vehicle-aided traffic flow scheduling, drone-aided visual inspections, etc. However, dynamic perception and cooperative decision making among these heterogeneous and resource-constrained mobile clients in IIoT remains a big challenge. In this article, we propose an incentive-based federated learning scheme for digital twin (DT)-driven industrial mobile crowdsensing. Specifically, we first design a DT-driven industrial mobile crowdsensing architecture to achieve dynamic perception of the complex IIoT environment, among heterogeneous and resource-constrained mobile clients. Second, we develop a novel incentive-based federated learning framework incorporated with a contract-based reputation mechanism and a Stackelberg-based interclient incentive mechanism, to optimize the model accuracy. Third, we devise a knowledge distillation algorithm for the federated learning framework, to address the heterogeneity of nonindependent and identically distributed (Non-IID) data. Extensive experiments on both MNIST/FEMNIST and CIFAR10/100 data sets demonstrate the outperformance of our proposed scheme, in terms of model accuracy, incentive fairness, and data compatibility, compared to state-of-the-art studies.