  Vehicular crowd intelligence (VCI) is an emerging research field. Facilitated
by state-of-the-art vehicular ad-hoc networks and artificial intelligence,
various VCI applications come to place, e.g., collaborative sensing,
positioning, and mapping. The collaborative property of VCI applications
generally requires data to be shared among participants, thus forming
network-wide intelligence. How to fulfill this process without compromising
data privacy remains a challenging issue. Although federated learning (FL) is a
promising tool to solve the problem, adapting conventional FL frameworks to VCI
is nontrivial. First, the centralized model aggregation is unreliable in VCI
because of the existence of stragglers with unfavorable channel conditions.
Second, existing FL schemes are vulnerable to Non-IID data, which is
intensified by the data heterogeneity in VCI. This paper proposes a novel
federated learning framework called RaftFed to facilitate privacy-preserving
VCI. The experimental results show that RaftFed performs better than baselines
regarding communication overhead, model accuracy, and model convergence.
