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：室内环境中联网设备的普及为众多以定位服务为关键使能技术的室内应用开辟了空间。然而，随着隐私问题和资源约束的出现，设计大多数应用所要求的高精度定位系统变得更具挑战性。为克服上述挑战，本文提出了一种基于深度神经网络的联邦学习（FL）框架，用于层次化三维室内定位。首先，我们阐明了在多建筑、多楼层室内环境中利用楼层与建筑间层次关系的重要性。随后，提出了一种FL框架来训练所设计的层次化模型。性能评估表明，采用层次化学习方案后，定位精度相比非层次化方法最高可提升24.06%。同时，建筑和楼层的预测准确率分别达到99.90%和94.87%。借助所提出的FL框架，我们能够实现与集中式训练接近的性能特性，定位误差仅增加7.69%。此外，可扩展性研究表明，当更多设备参与训练时，FL系统的精度会进一步提升。