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

As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. The authors 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, the authors 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. The authors 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, the authors 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 the authors scheme.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01782863
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Sep 23 2021 2:45PM