Due to privacy issues and the scattered nature of data produced by vehicles,
the Internet of Vehicles (IOV) poses considerable hurdles for data collecting. In this
chapter, we examine the idea of “Federated Learning on Wheels” (FLoW), which
provides a decentralised method for IOV data collection with a focus on privacy.
FLOW makes use of the onboard computer resources of cars to carry out model
training locally, making sure that private information stays on the cars and is not shared
with a centralised server. This strategy overcomes the shortcomings of conventional
centralised data collecting approaches while simultaneously protecting user privacy.
We examine the fundamentals of federated learning and how they relate to IOV,
highlighting the advantages of maintaining privacy. We also look at secure aggregation
procedures and confidentiality safeguards as additional methods for privacy-enhanced
data acquisition in FLOW. Additionally, we emphasise the significance of accuracy
and performance issues in decentralised contexts and use examples that illustrate
FLOW's usefulness. We also explore security and trust issues, talking about possible
weaknesses and methods to secure the reliability of participants and model updates. We
also consider how blockchain technology may be incorporated for improved security
and openness. We conclude by discussing FLOW future directions, difficulties, and
ethical issues in order to shed light on its possible significance and legal ramifications.
Overall, this chapter clarifies the relevance of Federated Learning to Wheels as a
ground-breaking approach to data collecting with increased privacy in the Internet of
Vehicles.
Keywords: Blockchain, Federated learning, Internet of vehicles (IOV), Privacy preservation.
