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Coupled with the rise of Deep Learning, the wealth of data and enhancedcomputation capabilities of Internet of Vehicles (IoV) components enableeffective Artificial Intelligence (AI) based models to be built. Beyond grounddata sources, Unmanned Aerial Vehicles (UAVs) based service providers for datacollection and AI model training, i.e., Drones-as-a-Service, is increasinglypopular in recent years. However, the stringent regulations governing dataprivacy potentially impedes data sharing across independently owned UAVs. Tothis end, we propose the adoption of a Federated Learning (FL) based approachto enable privacy-preserving collaborative Machine Learning across a federationof independent DaaS providers for the development of IoV applications, e.g.,for traffic prediction and car park occupancy management. Given the informationasymmetry and incentive mismatches between the UAVs and model owners, weleverage on the self-revealing properties of a multi-dimensional contract toensure truthful reporting of the UAV types, while accounting for the multiplesources of heterogeneity, e.g., in sensing, computation, and transmissioncosts. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAVto each subregion. The simulation results validate the incentive compatibilityof our contract design, and shows the efficiency of our matching, thusguaranteeing profit maximization for the model owner amid informationasymmetry.

Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach