FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
This work addresses parking management efficiency for operators and users, but it is incremental as it combines existing techniques like federated learning and reinforcement learning in a new application.
The paper tackles parking space estimation by applying federated learning to train an LSTM model among parking lot operators without sharing raw data, and manages parked vehicle-assisted edge computing via a Stackelberg game and multi-agent deep reinforcement learning, with numerical results showing effectiveness.
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.