论文标题
在边缘网络提供位置信息：一种基于联合学习的方法
Providing Location Information at Edge Networks: A Federated Learning-Based Approach
论文作者
论文摘要
最近，移动边缘计算的开发使令人振奋的边缘人工智能（AI）具有快速响应和低沟通成本。边缘设备的位置信息对于在许多情况下支持Edge AI至关重要，例如智能家居，智能运输系统和集成的医疗保健。利用深度学习智能的优势，集中的机器学习（ML）的定位技术引起了学术界和工业的激烈关注。但是，某些潜在问题，例如位置信息泄漏和庞大的数据流量，限制了其应用程序。幸运的是，有望减轻这些担忧的新兴新兴隐私分布式ML机制，称为联邦学习（FL）。在本文中，我们说明了一个基于FL的本地化系统以及Edge Networks的涉及实体的框架。此外，这种系统的优势是详细阐述的。在实际实施IT时，我们研究了与系统级解决方案相关的特定现场问题，这些问题在现实字数据库中得到了进一步证明。此外，概述了该领域的未来挑战性开放问题。
Recently, the development of mobile edge computing has enabled exhilarating edge artificial intelligence (AI) with fast response and low communication cost. The location information of edge devices is essential to support the edge AI in many scenarios, like smart home, intelligent transportation systems and integrated health care. Taking advantages of deep learning intelligence, the centralized machine learning (ML)-based positioning technique has received heated attention from both academia and industry. However, some potential issues, such as location information leakage and huge data traffic, limit its application. Fortunately, a newly emerging privacy-preserving distributed ML mechanism, named federated learning (FL), is expected to alleviate these concerns. In this article, we illustrate a framework of FL-based localization system as well as the involved entities at edge networks. Moreover, the advantages of such system are elaborated. On practical implementation of it, we investigate the field-specific issues associated with system-level solutions, which are further demonstrated over a real-word database. Moreover, future challenging open problems in this field are outlined.
