  Federated learning (FL) can empower Internet-of-Vehicles (IoV) networks by
leveraging smart vehicles (SVs) to participate in the learning process with
minimum data exchanges and privacy disclosure. The collected data and learned
knowledge can help the vehicular service provider (VSP) improve the global
model accuracy, e.g., for road safety as well as better profits for both VSP
and participating SVs. Nonetheless, there exist major challenges when
implementing the FL in IoV networks, such as dynamic activities and diverse
quality-of-information (QoI) from a large number of SVs, VSP's limited payment
budget, and profit competition among SVs. In this paper, we propose a novel
dynamic FL-based economic framework for an IoV network to address these
challenges. Specifically, the VSP first implements an SV selection method to
determine a set of the best SVs for the FL process according to the
significance of their current locations and information history at each
learning round. Then, each selected SV can collect on-road information and
offer a payment contract to the VSP based on its collected QoI. For that, we
develop a multi-principal one-agent contract-based policy to maximize the
profits of the VSP and learning SVs under the VSP's limited payment budget and
asymmetric information between the VSP and SVs. Through experimental results
using real-world on-road datasets, we show that our framework can converge 57%
faster (even with only 10% of active SVs in the network) and obtain much higher
social welfare of the network (up to 27.2 times) compared with those of other
baseline FL methods.
