Although distracted driving recognition is of great significance to traffic safety, drivers are reluctant to provide their own personalized driving data to machine learning because of privacy protection. How to improve the accuracy of distracted driving recognition on the basis of ensuring privacy protection? To address the issue, we proposed the federated shallow-CNN recognition framework (Fed-SCNN). Firstly, a hybrid model is established on the user-side through DNN and shallow-CNN, which recognizes the data of the in-vehicle images and uploads the encrypted parameters to the cloud. Secondly, the cloud server performs federated learning on major parameters through DNN to build a global cloud model. Finally, The DNN is updated in the user-side to further optimize the hybrid model. The above three steps are cycled to iterate the local hybrid model continuously. The Fed-SCNN framework is a dynamic learning process that addresses the two major issues of data isolation and privacy protection. Compared with the existing machine learning method, Fed-SCNN has great advantages in accuracy, safety, and efficiency and has important application value in the field of safe driving.

中文翻译：

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Fed-SCNN：用于分散驾驶的联合浅层CNN识别框架

尽管分散注意力的驾驶识别对交通安全具有重要意义，但由于隐私保护，驾驶员不愿将自己的个性化驾驶数据提供给机器学习。在确保隐私保护的基础上，如何提高分心驾驶识别的准确性？为了解决该问题，我们提出了联合浅层CNN识别框架（Fed-SCNN）。首先，通过DNN和浅层CNN在用户侧建立混合模型，该模型识别车内图像数据并将加密后的参数上传到云中。其次，云服务器通过DNN对主要参数执行联合学习，以建立全局云模型。最后，在用户端更新DNN，以进一步优化混合模型。循环上述三个步骤以连续迭代局部混合模型。Fed-SCNN框架是一个动态学习过程，致力于解决数据隔离和隐私保护这两个主要问题。与现有的机器学习方法相比，Fed-SCNN在准确性，安全性和效率上具有很大的优势，在安全驾驶领域具有重要的应用价值。