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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在支持无人机的车联网中实现通信高效联合学习的联合拍卖联盟形成框架

由于车辆，路边单元（RSU）和智能设备等车辆互联网（IoV）组件的高级功能以及生成的数据量不断增加，联合学习（FL）可以实现隐私保护，因此成为有前途的工具-可以在IoV中实现的机器学习。但是，FL的性能会受到通信链路故障和节点丢失的困扰，尤其是在需要连续交换模型参数时。因此，我们建议使用无人飞行器（UAV）作为无线中继，以促进IoV组件与FL服务器之间的通信，从而提高FL的准确性。但是，单个UAV可能没有足够的资源来为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IoV组件组的问题。具体而言，制定联盟形成博弈以最大化无人机的个人收益之和。提出了联合拍卖联盟形成算法，实现了无人机联盟的稳定划分，采用拍卖方案解决了无人机联盟的分配问题。设计拍卖方案时要考虑IoV组件相对于异构UAV的偏好。仿真结果表明，由于无人飞行器的利润最大化行为，所有无人飞行器都加入一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，