FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing

TL;DR

FedParking leverages federated learning and edge computing with parked vehicles to accurately estimate parking spaces while preserving data privacy and optimizing resource management.

Contribution

This paper introduces FedParking, a novel federated learning framework for parking estimation combined with a PVEC management scheme using multi-agent deep reinforcement learning.

Findings

Effective parking space estimation accuracy demonstrated

PVEC management improves resource utilization and incentive mechanisms

Distributed, privacy-preserving approach reaches Stackelberg equilibrium

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.