FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices

TL;DR

FedHIL is a federated learning framework designed to enhance indoor localization accuracy across heterogeneous mobile devices by addressing device variability and data noise, outperforming existing methods in real-world tests.

Contribution

This paper introduces FedHIL, a novel federated learning approach that maintains localization accuracy in heterogeneous environments and noisy data scenarios, a challenge not effectively handled by prior models.

Findings

FedHIL achieves 1.62x better accuracy than previous FL-based localization methods.

It effectively handles device heterogeneity and noisy data in diverse indoor environments.

Experimental results demonstrate significant improvements over state-of-the-art frameworks.

Abstract

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.