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.

中文翻译：

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用于 UAV 支持的车联网中高效通信联合学习的联合拍卖-联盟形成框架

由于车辆、路边单元 (RSU) 和智能设备等车联网 (IoV) 组件的先进功能以及生成的数据量不断增加，联邦学习 (FL) 成为一种很有前途的工具，因为它可以实现隐私- 保留可以在车联网中实施的机器学习。然而，FL 的性能受到通信链路故障和节点丢失的影响，尤其是在需要连续交换模型参数时。因此，我们建议使用无人驾驶飞行器 (UAV) 作为无线中继，以促进车联网组件与 FL 服务器之间的通信，从而提高 FL 的准确性。然而，单个无人机可能没有足够的资源为 FL 过程的所有迭代提供服务。在本文中，我们提出了一个联合拍卖联盟形成框架，以解决将无人机联盟分配给车联网组件组的问题。具体而言，联盟形成博弈被制定为最大化无人机的个体利润总和。提出联合拍卖-联盟形成算法来实现无人机联盟的稳定划分，其中应用拍卖方案来解决无人机联盟的分配。拍卖方案旨在考虑车联网组件对异构无人机的偏好。模拟结果表明，由于无人机的利润最大化行为，所有无人机加入单个联盟的大联盟并不总是稳定的。此外，我们表明，随着无人机合作成本的增加，