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Cited by 115Smart Parking Systems ResearchPrivacy-Preserving Technologies in DataIoT and Edge/Fog Computing

FedParking: A Federated Learning Based Parking Space Estimation With Parked Vehicle Assisted Edge Computing

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Huang, Xumin, Li, Peichun, Yu, Rong, Wu, Yuan, Xie, Kan, Xie, Shengli·2021·IEEE Transactions on Vehicular Technology
DOI 10.1109/tvt.2021.3098170
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Abstract

As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parking space estimation without exchanging the raw data. Furthermore, we investigate the management of Parked Vehicle assisted Edge Computing (PVEC) by FedParking. In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking. We formulate the interactions among the PLOs and vehicles as a multi-lead multi-follower Stackelberg game. Considering the dynamic arrivals of the vehicles and time-varying parking capacity constraints, we present a multi-agent deep reinforcement learning approach to gradually reach the Stackelberg equilibrium in a distributed yet privacy-preserving manner. Finally, numerical results are provided to demonstrate the effectiveness and efficiency of our scheme.

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  • Authors in [51] proposed a FL framework for real-time parking predictions, allowing vehicles to forecast availability without sharing sensitive data.

    “Authors in [51] proposed a FL framework for real-time parking predictions, allowing vehicles to forecast availability without sharing sensitive data.”

    From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [51]

    The Introduction (p. 2) and Section B (p. 3) support a federated-learning framework for real-time parking-space forecasts without exchanging raw data. They identify parking lot operators—not vehicles—as the parties training the model and forecasting availability; vehicles instead provide edge-computing resources. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 27

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Cited by

Sorted by citations

Authors
Huang, X., Li, P., Yu, R., Wu, Y., Xie, K., & Xie, S.
Affiliations
  • Guangdong University of Technology (CN)
  • University of Macau (MO)
ORCID
Venue
IEEE Transactions on Vehicular Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2021-09
Volume
70
Issue
9
Pages
9355-9368
ISSN
  • 0018-9545 (ISSN-L)
  • 1939-9359
DOI
10.1109/tvt.2021.3098170
OpenAlex
W3189867955
Cited by
115
References
6
Citations by year
2026: 7 · 2025: 21 · 2024: 26 · 2023: 29 · 2022: 27 · 2021: 5
FWCI
9.97
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Vehicular Technology (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (62001125, 61973087, 61971148, U1911401)
  • Science and Technology Development Fund of Macau SAR (0060/2019/A1)
  • Research Grant of University of Macau (MYRG2018-00237-FST)
  • Natural Science Foundation of Guangxi Province (2018GXNSFDA281013)
  • Foundation for Science and Technology Project of Guilin City (20190214-3)
  • State Key Laboratory of Synthetical Automation for Process Industries (2020-KF-21-02)
Topics
  • Physical Sciences › Engineering › Building and Construction › Smart Parking Systems Research
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
Keywords
federated learning, parking management, long short-term memory, computation offloading, incentive mechanisms, Stackelberg game, multi-agent deep reinforcement learning, privacy preservation

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