  Due to the advanced capabilities of the Internet of Vehicles (IoV) components
such as vehicles, Roadside Units (RSUs) and smart devices as well as the
increasing amount of data generated, Federated Learning (FL) becomes a
promising tool given that it enables privacy-preserving machine learning that
can be implemented in the IoV. However, the performance of the FL suffers from
the failure of communication links and missing nodes, especially when
continuous exchanges of model parameters are required. Therefore, we propose
the use of Unmanned Aerial Vehicles (UAVs) as wireless relays to facilitate the
communications between the IoV components and the FL server and thus improving
the accuracy of the FL. However, a single UAV may not have sufficient resources
to provide services for all iterations of the FL process. In this paper, we
present a joint auction-coalition formation framework to solve the allocation
of UAV coalitions to groups of IoV components. Specifically, the coalition
formation game is formulated to maximize the sum of individual profits of the
UAVs. The joint auction-coalition formation algorithm is proposed to achieve a
stable partition of UAV coalitions in which an auction scheme is applied to
solve the allocation of UAV coalitions. The auction scheme is designed to take
into account the preferences of IoV components over heterogeneous UAVs. The
simulation results show that the grand coalition, where all UAVs join a single
coalition, is not always stable due to the profit-maximizing behavior of the
UAVs. In addition, we show that as the cooperation cost of the UAVs increases,
the UAVs prefer to support the IoV components independently and not to form any
coalition.
