An essential consideration in cyber-physical systems (CPS) is the ability to support secure communication services, such as points of interest (POI) microservices. Existing approaches to support secure POI microservices generally rely on anonymity and/or differential privacy technologies. There are, however, a number of known limitations with such approaches. Hence, this work presents a deep-federated-learning-based framework for securing POI microservices in CPS. In order to enhance data security, the system architecture is designed to isolate the cloud center from accessing user data on edge nodes, and an interactive training mechanism is introduced between the cloud center and edge nodes. Specifically, edge nodes pre-train reliable deep-learning-based models for users, and the cloud server coordinates parameter updating via federated learning. The connected and isolated structure between cloud center and edges facilitates deep federated learning. Finally, we implement and evaluate the performance of our proposed approach using two real-world POI-related datasets. The results show that our proposed approach achieves optimal scheduling performance and demonstrates its practical utility.

中文翻译：

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用于网络物理系统的深度联合学习增强安全 POI 微服务

网络物理系统 (CPS) 的一个基本考虑因素是支持安全通信服务的能力，例如兴趣点 (POI) 微服务​​。支持安全 POI 微服务的现有方法通常依赖于匿名和/或差分隐私技术。然而，这些方法存在许多已知的限制。因此，这项工作提出了一个基于深度联合学习的框架，用于保护 CPS 中的 POI 微服务。为了增强数据安全性，系统架构设计将云中心与边缘节点上的用户数据访问隔离，并在云中心和边缘节点之间引入交互训练机制。具体来说，边缘节点为用户预训练可靠的基于深度学习的模型，云服务器通过联邦学习协调参数更新。云中心和边缘之间的连接和隔离结构有助于深度联邦学习。最后，我们使用两个真实世界的 POI 相关数据集来实施和评估我们提出的方法的性能。结果表明，我们提出的方法实现了最佳的调度性能，并证明了它的实用性。