  The rapid increase of the data scale in Internet of Vehicles (IoV) system
paradigm, hews out new possibilities in boosting the service quality for the
emerging applications through data sharing. Nevertheless, privacy concerns are
major bottlenecks for data providers to share private data in traditional IoV
networks. To this end, federated learning (FL) as an emerging learning
paradigm, where data providers only send local model updates trained on their
local raw data rather than upload any raw data, has been recently proposed to
build a privacy-preserving data sharing models. Unfortunately, by analyzing on
the differences of uploaded local model updates from data providers, private
information can still be divulged, and performance of the system cannot be
guaranteed when partial federated nodes executes malicious behavior.
Additionally, traditional cloud-based FL poses challenges to the communication
overhead with the rapid increase of terminal equipment in IoV system. All these
issues inspire us to propose an autonomous blockchain empowered
privacy-preserving FL framework in this paper, where the mobile edge computing
(MEC) technology was naturally integrated in IoV system.
