FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
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.
Cite
@article{arxiv.2110.12876,
title = {FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing},
author = {Xumin Huang and Peichun Li and Rong Yu and Yuan Wu and Kan Xie and Shengli Xie},
journal= {arXiv preprint arXiv:2110.12876},
year = {2021}
}
Comments
Accepted in IEEE TVT, small bugs in Sec. V-B are corrected in this version. Copyright (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses