The joint intelligence ranking of intelligent systems like autonomous driving is of great importance for building a more general, extensive, and universally accepted intelligence evaluation scheme. However, due to issues such as privacy security and industry or area competition, the integration of isolated test results may face large unimaginable difficulty in information security and encrypted model training. To address this, we derive the federated multiplicative update (FMU) algorithm with boundary constraints to solve the nonnegative matrix factorization based joint intelligence ranking. The encrypted learning process is developed to alternate original computation steps in multiplicative update algorithms. Owning feasible property for the fast convergence and secure exchange of variables, the proposed framework outperforms the previous work on both real and simulated data. Further experimental analysis reveals that the introduced federated mechanism does not harm the overall time efficiency.

中文翻译：

---

联合乘法更新的联合情报排名

自动驾驶等智能系统的联合智能排名对于构建更通用、更广泛、更普遍接受的智能评估方案具有重要意义。然而，由于隐私安全和行业或地区竞争等问题，孤立测试结果的整合可能在信息安全和加密模型训练方面面临难以想象的巨大困难。为了解决这个问题，我们推导出具有边界约束的联邦乘法更新 (FMU) 算法来解决基于非负矩阵分解的联合智能排名。加密学习过程被开发来交替乘法更新算法中的原始计算步骤。拥有快速收敛和安全交换变量的可行属性，提议的框架在真实数据和模拟数据上都优于之前的工作。进一步的实验分析表明，引入的联邦机制不会损害整体时间效率。