Quality-Aware User Recruitment Based on Federated Learning in Mobile Crowd Sensing

With the rapid development of mobile devices, the use of Mobile Crowd Sensing (MCS) mode has become popular to complete more intelligent and complex sensing tasks. However, large-scale data collection may reduce the quality of sensed data. Thus, quality control is a key problem in MCS. With the emergence of the federated learning framework, the number of complex intelligent calculations that can be completed on mobile devices has increased. In this study, we formulate a quality-aware user recruitment problem as an optimization problem. We predict the quality of sensed data from different users by analyzing the correlation between data and context information through federated learning. Furthermore, the lightweight neural network model located on mobile terminals is used. Based on the prediction of sensed quality, we develop a user recruitment algorithm that runs on the cloud platform through terminal-cloud collaboration. The performance of the proposed method is evaluated through simulations. Results show that compared with existing algorithms, i.e., Random Adaptive Greedy algorithm for User Recruitment (RAGUR) and Context-Aware Tasks Allocation (CATA), the proposed method improves the quality of sensed data by 23.5 $\mathrm{\%}$ and 38.8 $\mathrm{\%}$, respectively.