  A new federated learning (FL) framework enabled by large-scale wireless
connectivity is proposed for designing the autonomous controller of connected
and autonomous vehicles (CAVs). In this framework, the learning models used by
the controllers are collaboratively trained among a group of CAVs. To capture
the varying CAV participation in the FL training process and the diverse local
data quality among CAVs, a novel dynamic federated proximal (DFP) algorithm is
proposed that accounts for the mobility of CAVs, the wireless fading channels,
as well as the unbalanced and nonindependent and identically distributed data
across CAVs. A rigorous convergence analysis is performed for the proposed
algorithm to identify how fast the CAVs converge to using the optimal
autonomous controller. In particular, the impacts of varying CAV participation
in the FL process and diverse CAV data quality on the convergence of the
proposed DFP algorithm are explicitly analyzed. Leveraging this analysis, an
incentive mechanism based on contract theory is designed to improve the FL
convergence speed. Simulation results using real vehicular data traces show
that the proposed DFP-based controller can accurately track the target CAV
speed over time and under different traffic scenarios. Moreover, the results
show that the proposed DFP algorithm has a much faster convergence compared to
popular FL algorithms such as federated averaging (FedAvg) and federated
proximal (FedProx). The results also validate the feasibility of the
contract-theoretic incentive mechanism and show that the proposed mechanism can
improve the convergence speed of the DFP algorithm by 40% compared to the
baselines.
