  Federated learning (FL) is the promising privacy-preserve approach to
continually update the central machine learning (ML) model (e.g., object
detectors in edge servers) by aggregating the gradients obtained from local
observation data in distributed connected and automated vehicles (CAVs). The
incentive mechanism is to incentivize individual selfish CAVs to participate in
FL towards the improvement of overall model accuracy. It is, however,
challenging to design the incentive mechanism, due to the complex correlation
between the overall model accuracy and unknown incentive sensitivity of CAVs,
especially under the non-independent and identically distributed (Non-IID) data
of individual CAVs. In this paper, we propose a new learn-to-incentivize
algorithm to adaptively allocate rewards to individual CAVs under unknown
sensitivity functions. First, we gradually learn the unknown sensitivity
function of individual CAVs with accumulative observations, by using
compute-efficient Gaussian process regression (GPR). Second, we iteratively
update the reward allocation to individual CAVs with new sampled gradients,
derived from GPR. Third, we project the updated reward allocations to comply
with the total budget. We evaluate the performance of extensive simulations,
where the simulation parameters are obtained from realistic profiling of the
CIFAR-10 dataset and NVIDIA RTX 3080 GPU. The results show that our proposed
algorithm substantially outperforms existing solutions, in terms of accuracy,
scalability, and adaptability.
