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.
|Number of pages||19|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|Early online date||11 Dec 2020|
|Publication status||Published - Apr 2021|
Bibliographical noteFunding Information:
Manuscript received April 13, 2020; revised July 14, 2020 and September 9, 2020; accepted October 30, 2020. Date of publication December 11, 2020; date of current version March 31, 2021. This work was supported in part by the Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), National Research Foundation, Singapore, through its AI Singapore Programme (AISG) under Award AISG-GC-2019-003, in part by the Singapore Energy Market Authority (EMA), Energy Resilience, under Grant NRF2017EWT-EP003-041, in part by the Singapore under Grant NRF2015-NRF-ISF001-2277, in part by WASP/NTU under Grant M4082187 (4080), in part by the Singapore Ministry of Education (MOE) Tier 1 under Grant RG16/20, and in part by the Macao Science and Technology Development Fund under Macao Funding Scheme for key research and development projects under Grant 0025/2019/AKP. The Associate Editor for this article was Y. Zhang. (Corresponding author: Zehui Xiong.) Jer Shyuan Ng and Wei Yang Bryan Lim are with the Alibaba Group and Alibaba-NTU Joint Research Institute, Nanyang Technological University, Singapore 639798.
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- Federated learning
- Internet of vehicles
- unmanned aerial vehicles