Incentive-Based Federated Learning for Digital-Twin-Driven Industrial Mobile Crowdsensing

Abstract

Mobile crowdsensing has empowered the Industrial Internet of Things (IIoT) in many ways, such as vehicle-aided traffic flow scheduling and 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 paper, we propose an incentive-based federated learning scheme for digital twin driven industrial mobile crowdsensing. Specifically, we first design a digital twin 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 inter-client incentive mechanism, to optimize the model accuracy. Third, we devise a knowledge distillation algorithm for the federated learning framework, to address the heterogeneity of non-independent and identically distributed (Non-IID) data. Extensive experiments on both MNIST/FEMNIST and CIFAR10/100 datasets demonstrate the outperformance of our proposed scheme, in terms of model accuracy, incentive fairness, and data compatibility, compared to state-of-the-art studies.