  Coupled with the rise of Deep Learning, the wealth of data and enhanced
computation capabilities of Internet of Vehicles (IoV) 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, is increasingly
popular in recent years. However, the stringent regulations governing data
privacy potentially impedes data sharing across independently owned UAVs. To
this end, we propose the adoption of a Federated Learning (FL) based approach
to enable privacy-preserving collaborative Machine Learning across a federation
of independent DaaS providers for the development of IoV applications, e.g.,
for traffic prediction and car park occupancy management. Given the information
asymmetry and incentive mismatches between the UAVs and model owners, we
leverage on the self-revealing properties of a multi-dimensional contract to
ensure truthful reporting of the UAV types, while accounting for the multiple
sources of heterogeneity, e.g., in sensing, computation, and transmission
costs. Then, we adopt the Gale-Shapley algorithm to match the lowest cost UAV
to each subregion. The simulation results validate the incentive compatibility
of our contract design, and shows the efficiency of our matching, thus
guaranteeing profit maximization for the model owner amid information
asymmetry.
