The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers. However, as privacy issues and resource constraints arise, it becomes more challenging to design accurate positioning systems as required by most applications. To overcome the latter challenges, we present in this paper, a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. Indeed, we firstly shed light on the prominence of exploiting the hierarchy between floors and buildings in a multi-building and multi-floor indoor environment. Then, we propose an FL framework to train the designed hierarchical model. The performance evaluation shows that by adopting a hierarchical learning scheme, we can improve the localization accuracy by up to 24.06% compared to the non-hierarchical approach. We also obtain a building and floor prediction accuracy of 99.90% and 94.87% respectively. With the proposed FL framework, we can achieve a near-performance characteristic as of the central training with an increase of only 7.69% in the localization error. Moreover, the conducted scalability study reveals that the FL system accuracy is improved when more devices join the training.
翻译:室内环境连通装置的泛滥使室内应用层层成为众多的室内应用程序,定位服务是关键的促进因素。然而,随着隐私问题和资源限制的出现,根据大多数应用程序的要求设计准确的定位系统就更具挑战性。为了克服后一挑战,我们在本文件中提出使用深层神经网络的3D室内本地化3D级联合学习框架。事实上,我们首先揭示了在多层建筑和多层室内环境中利用楼层和建筑物之间的等级的突出地位。然后,我们提出了一个FL框架,用于培训设计的等级模型。绩效评估表明,通过采用等级学习计划,我们可以提高本地化准确性,比非等级化方法提高24.06%。我们还获得了建筑和地板预测准确率,分别为99.90%和94.87%。根据拟议的FL框架,我们可以实现中央培训的接近性特征,在本地化错误中仅增加7.69%。此外,进行的规模分析表明,如果更多的设备加入培训,FL系统的准确性将得到改善。</s>