  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.
