Hedonic Coalition Formation Game and Contract-based Federated Learning in UAV-Assisted Internet of Things

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective Artificial Intelligence (AI)-based models to be built. Beyond ground data sources, Unmanned Aerial Vehicles (UAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned UAVs. To this end, we propose in this paper a Federated Learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with UAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of UAV capabilities while optimizing the global model owner’s profits. The performance-based UAV type is also defined to offer a practical measure for heterogeneous UAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks.