2 research outputs found
CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization
Indoor localization has become increasingly vital for many applications from
tracking assets to delivering personalized services. Yet, achieving pinpoint
accuracy remains a challenge due to variations across indoor environments and
devices used to assist with localization. Another emerging challenge is
adversarial attacks on indoor localization systems that not only threaten
service integrity but also reduce localization accuracy. To combat these
challenges, we introduce CALLOC, a novel framework designed to resist
adversarial attacks and variations across indoor environments and devices that
reduce system accuracy and reliability. CALLOC employs a novel adaptive
curriculum learning approach with a domain specific lightweight scaled-dot
product attention neural network, tailored for adversarial and variation
resilience in practical use cases with resource constrained mobile devices.
Experimental evaluations demonstrate that CALLOC can achieve improvements of up
to 6.03x in mean error and 4.6x in worst-case error against state-of-the-art
indoor localization frameworks, across diverse building floorplans, mobile
devices, and adversarial attacks scenarios
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices
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