论文标题
EFEDDNN：基于合奏的联合深层神经网络用于轨迹模式推断
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference
论文作者
论文摘要
作为智能移动系统中最重要的数据源，GPS轨迹可以帮助识别用户旅行模式。但是，这些GPS数据集可能包含用户的私人信息（例如家庭位置），从而阻止许多用户与第三方共享其私人信息。因此，在保护用户隐私的同时确定旅行模式是一个重要的问题。为了应对这一挑战，我们使用联合学习（FL），这是一种隐私的机器学习技术，旨在通过访问用户的本地训练模型而不是其原始数据来协作培训强大的全球模型。具体而言，我们设计了一种新型的基于合奏的联合深层神经网络（EFEDDNN）。合奏方法结合了通过用户通过FL学到的不同模型的输出，并显示出超过文献中报道的可比较模型的准确性。对来自蒙特利尔的真实世界开放式数据集的广泛实验研究表明，所提出的推论模型可以在不损害隐私的情况下准确地识别用户的旅行方式。
As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private information (e.g., home location), preventing many users from sharing their private information with a third party. Hence, identifying travel modes while protecting users' privacy is a significant issue. To address this challenge, we use federated learning (FL), a privacy-preserving machine learning technique that aims at collaboratively training a robust global model by accessing users' locally trained models but not their raw data. Specifically, we designed a novel ensemble-based Federated Deep Neural Network (eFedDNN). The ensemble method combines the outputs of the different models learned via FL by the users and shows an accuracy that surpasses comparable models reported in the literature. Extensive experimental studies on a real-world open-access dataset from Montreal demonstrate that the proposed inference model can achieve accurate identification of users' mode of travel without compromising privacy.
