Vehicular networks enable vehicles support real-time vehicular applications
through training data. Due to the limited computing capability, vehicles
usually transmit data to a road side unit (RSU) at the network edge to process
data. However, vehicles are usually reluctant to share data with each other due
to the privacy issue. For the traditional federated learning (FL), vehicles
train the data locally to obtain a local model and then upload the local model
to the RSU to update the global model, thus the data privacy can be protected
through sharing model parameters instead of data. The traditional FL updates
the global model synchronously, i.e., the RSU needs to wait for all vehicles to
upload their models for the global model updating. However, vehicles may
usually drive out of the coverage of the RSU before they obtain their local
models through training, which reduces the accuracy of the global model. It is
necessary to propose an asynchronous federated learning (AFL) to solve this
problem, where the RSU updates the global model once it receives a local model
from a vehicle. However, the amount of data, computing capability and vehicle
mobility may affect the accuracy of the global model. In this paper, we jointly
consider the amount of data, computing capability and vehicle mobility to
design an AFL scheme to improve the accuracy of the global model. Extensive
simulation experiments have demonstrated that our scheme outperforms the FL
schemeComment: This paper has been submitted to WCS