Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework called FedHIL. Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy. FedHIL integrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62x better localization accuracy on average than the best performing FL-based indoor localization framework from prior work.

翻译：室内定位在应急响应、仓库管理和增强现实体验等应用中发挥着关键作用。通过在移动设备上部署基于机器学习（ML）的室内定位框架，用户可以在各种室内及地下环境中实现自我定位。然而，由于移动设备的硬件和软件栈存在异构性，可能导致定位结果不一致且不准确，因此实现精准的室内定位颇具挑战。传统ML模型还严重依赖初始训练数据，使其在室内环境动态变化时性能易发生退化。为解决设备异构性和缺乏自适应性的挑战，我们提出了一种名为FedHIL的新型嵌入式ML框架。该框架融合了室内定位与联邦学习（FL），可在设备异构环境中提升室内定位精度，同时保护用户数据隐私。FedHIL集成了领域特定选择性权重调整方法，即使在存在极端噪声数据的情况下，也能在FL过程中保持ML模型在室内定位中的性能。在多种真实室内环境和异构移动设备上的实验评估表明，FedHIL的性能优于现有最先进的FL及非FL室内定位框架。FedHIL的平均定位精度较先前工作中性能最佳的基于FL的室内定位框架提升了1.62倍。