Communication-Efficient Federated Learning in UAV-enabled IoV: A Joint Auction-Coalition Approach

Jer Shyuan Ng, Wei Yang Bryan Lim, Hong Ning Dai, Zehui Xiong, Jianqiang Huang, Dusit Niyato, Xian Sheng Hua, Cyril Leung, Chunyan Miao

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

5 Citations (Scopus)

Abstract

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. However, the performance of the FL suffers from the failure of communication links and missing nodes. 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 for all iterations of the FL process. In this paper, we present a joint auction-coalition formation framework. The joint auctioncoalition formation algorithm is proposed to achieve a stable partition of UAV coalitions in which an auction scheme is applied. 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 profitmaximizing behavior of the UAVs. In addition, we show that as the cooperation cost of the UAVs increases, the UAVs prefer not to form any coalition.

Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781728182988
ISBN (Print)9781728182995
DOIs
Publication statusPublished - Dec 2020
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 7 Dec 202011 Dec 2020
https://ieeexplore.ieee.org/xpl/conhome/9322055/proceeding

Publication series

NameIEEE Global Communications Conference, GLOBECOM - Proceedings

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period7/12/2011/12/20
Internet address

Scopus Subject Areas

  • Media Technology
  • Modelling and Simulation
  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Safety, Risk, Reliability and Quality

User-Defined Keywords

  • Auction
  • Coalition
  • Federated Learning
  • Internet of Vehicles
  • Unmanned Aerial Vehicles

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