Federated learning based on Stackelberg game in unmanned-aerial-vehicle-enabled mobile edge computing

Sudden medical safety accidents or large-scale outbreaks of epidemics will lead to a surge in traffic near hospitals and other medical infrastructure, and traditional edge base stations cannot cope with sudden traffic demands. In addition, the patient's data of health information is private, it is necessary to protect the patient's health information. Therefore, aiming at the practical problems that edge base stations cannot cope with a large number of communication requests and how to protect patients' private data under sudden medical safety accidents, this paper combines hierarchical federated learning with UAV-assisted mobile edge computing environment to build a UAV-assisted MEC system architecture for federated learning. However, federated learning is closely related to participating nodes. Without satisfactory rewards, users and UAVs will be unwilling to consume computing resources and communication resources to participate in federated learning. Therefore, this paper introduced the Stackelberg game and incentive mechanism into federated learning. The interaction between user devices, UAV, and the base station is modeled as a Stackelberg game to determine the maximum amount of data required for model training and the number of user devices and UAV. The incentive mechanism determines the task allocation and reward allocation of federated learning and determines the maximum benefits of three participants in the game process to improve the quality of user devices and the performance of the federated learning model. Experimental results show that the proposed algorithm can inspire user devices, which have high-quality data, to participate in federated learning training, improve training accuracy and maximize social welfare.