FedNav: A Federated Learning Approach for Secure AIoT-enabled Inertial Odometry

Modern inertial measurement units (IMUs) in smartphones, while offering a pathway to innovative indoor positioning solutions, often suffer from significant drift errors, with typical error margins that preclude localization within a three-meter threshold. This limitation severely hinders the practical deployment of indoor positioning systems in critical areas such as shopping malls, hospitals, and office buildings. Traditional methods to enhance localization accuracy typically involve integrating complex sensor fusion technologies, such as UWB, Wi-Fi, or Bluetooth, further complicating such solutions’ adoption and scalability. Additionally, traditional deep learning techniques for enhancing IMU performance face challenges related to privacy protection, data collection, and training efficiency. To address these issues, we propose FedNav, a novel federated learning (FL)-based inertial localization approach that leverages IoT client devices’ computational power to enhance IMU performance while adhering to stringent privacy requirements. FedNav significantly improves pedestrian localization accuracy by reducing the average positioning error, ranging from 5% to over 25% on benchmark datasets and state-of-the-art models. It ensures robust privacy protection and efficient data utilization without additional hardware dependencies. Experimental evaluations across multiple inertial odometry datasets (OxIOD, RONIN, and RIDI) and real-world inertial data collected on the KAIST campus illustrate FedNav’s superior performance, achieving localization accuracies that outperform existing methods. FedNav thus offers a promising solution for secure, accurate, and robust indoor positioning in AIoT systems, demonstrating the practical benefits of federated learning in overcoming the prevailing challenges in the field.