An Incentive Assignment Scheme of UAV Clients for Federated Intelligent Inspection Based on Communication-Sensing-Computing Integration

The convergence of communication, sensing, and computing capabilities is a key trend in future 6th generation mobile (6 G) networks. Integrating unmanned aerial vehicles (UAVs) with federated learning can further enhance network performance in these areas while reducing resource overhead and protecting data privacy. However, due to limited spectrum resources and data heterogeneity, lack of client scheduling not only increases bandwidth pressure but also degrades training performance. Moreover, incentive allocation in federated learning directly influences whether UAVs accept client selection and participate in collaborative learning tasks. In order to solve the above problems, this paper designs an incentive assignment scheme for UAV clients in federated intelligent inspection based on communication-sensing-computing integration. This scheme comprehensively considers two dimensional metrics, client data quality and contribution value, for UAV incentive allocation and selection, hence abbreviated as the Multi Dimensional Scheme (MDS). MDS accounts for the communication, sensing, and computational energy consumption of UAVs, establishing a federated learning candidate pool through contract theory. Subsequently, UAVs that contribute more to model training are selected from the candidate pool via Bayesian optimization. Experiments conducted on multiple datasets show that, compared to existing methods, MDS effectively improves the accuracy of model training while reducing incentive costs.