Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles
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...
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Engineering::Computer science and engineering Computational Modeling Unmanned Aerial Vehicles Ng, Jer Shyuan Lim, Bryan Wei Yang Dai, Hong-Ning Xiong, Zehui Huang, Jianqiang Niyato, Dusit Hua, Xian-Sheng Leung, Cyril Miao, Chunyan Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Ng, Jer Shyuan Lim, Bryan Wei Yang Dai, Hong-Ning Xiong, Zehui Huang, Jianqiang Niyato, Dusit Hua, Xian-Sheng Leung, Cyril Miao, Chunyan |
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Article |
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Ng, Jer Shyuan Lim, Bryan Wei Yang Dai, Hong-Ning Xiong, Zehui Huang, Jianqiang Niyato, Dusit Hua, Xian-Sheng Leung, Cyril Miao, Chunyan |
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Ng, Jer Shyuan |
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Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles |
title_short |
Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles |
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Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles |
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Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles |
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Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles |
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joint auction-coalition formation framework for communication-efficient federated learning in uav-enabled internet of vehicles |
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2021 |
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https://hdl.handle.net/10356/150971 |
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sg-ntu-dr.10356-1509712021-06-08T02:27:38Z Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles Ng, Jer Shyuan Lim, Bryan Wei Yang Dai, Hong-Ning Xiong, Zehui Huang, Jianqiang Niyato, Dusit Hua, Xian-Sheng Leung, Cyril Miao, Chunyan School of Computer Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Engineering::Computer science and engineering Computational Modeling Unmanned Aerial Vehicles 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. AI Singapore Accepted version This research is supported, in part, by Alibaba Group through Alibaba Innovative Research (AIR) Program and Alibaba-NTU Singapore Joint Research Institute (JRI), National Research Foundation, Singapore, under its AI Singapore Programme (AISG Award No: AISG-GC-2019-003), Singapore Energy Market Authority (EMA), Energy Resilience, under Grant NRF2017EWT-EP003-041; and in part by the Singapore NRF2015-NRF-ISF001-2277. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. This research is also supported by WASP/NTU grant M4082187 (4080) and Singapore Ministry of Education (MOE) Tier 1 (RG16/20). The work described in this paper is also partially supported by Macao Science and Technology Development Fund under Macao Funding Scheme for Key R & D Projects (0025/2019/AKP). 2021-06-08T02:27:37Z 2021-06-08T02:27:37Z 2021 Journal Article Ng, J. S., Lim, B. W. Y., Dai, H., Xiong, Z., Huang, J., Niyato, D., Hua, X., Leung, C. & Miao, C. (2021). Joint auction-coalition formation framework for communication-efficient federated learning in UAV-enabled Internet of Vehicles. IEEE Transactions On Intelligent Transportation Systems, 22(4), 2326-2344. https://dx.doi.org/10.1109/TITS.2020.3041345 1558-0016 0000-0003-2772-8977 0000-0003-2150-5561 0000-0001-6165-4196 0000-0002-4440-941X 0000-0002-7442-7416 0000-0001-9911-2069 0000-0002-0300-3448 https://hdl.handle.net/10356/150971 10.1109/TITS.2020.3041345 2-s2.0-85097931116 4 22 2326 2344 en IEEE Transactions on Intelligent Transportation Systems © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TITS.2020.3041345 application/pdf |