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.

翻译：在统一的尺度上对各类驾驶员的驾驶表现进行评分，基于其日常行驶中的安全性和经济性，是驾驶员画像任务的关键。网联车辆为收集真实驾驶数据提供了机会，这有利于构建评分模型。然而，预标注分数的缺失阻碍了监督回归模型的使用，同时数据隐私问题妨碍了传统的基于云端的集中式学习进行模型训练的方式。为解决这些问题，本文提出了一种无需标签的无监督评分方法，与主观评分策略相比，该方法仍能保持公平性和客观性。随后，我们提出了一种基于车云协作的联邦学习框架，作为集中式学习的隐私友好型替代方案。该框架包含评分方法的一致性联邦版本，以减轻由本地数据统计异质性挑战导致的全局评分模型性能下降。理论分析与实验证明，我们的联邦评分模型与集中式学习版本效用一致，并能有效评估驾驶表现。