FedDriveScore: Federated Scoring Driving Behavior with a Mixture of
  Metric Distributions
Lin Lu
Scoring the driving performance of various drivers on a unified scale, based
on how safe or economical they drive on their daily trips, is essential for the
driver profile task. Connected vehicles provide the opportunity to collect
real-world driving data, which is advantageous for constructing scoring models.
However, the lack of pre-labeled scores impede the use of supervised regression
models and the data privacy issues hinder the way of traditionally
data-centralized learning on the cloud side for model training. To address
them, an unsupervised scoring method is presented without the need for labels
while still preserving fairness and objectiveness compared to subjective
scoring strategies. Subsequently, a federated learning framework based on
vehicle-cloud collaboration is proposed as a privacy-friendly alternative to
centralized learning. This framework includes a consistently federated version
of the scoring method to reduce the performance degradation of the global
scoring model caused by the statistical heterogeneous challenge of local data.
Theoretical and experimental analysis demonstrate that our federated scoring
model is consistent with the utility of the centrally learned counterpart and
is effective in evaluating driving performance.