<ul data-eligibleForWebStory="false"><li>A new Privacy-Preserving Indoor Localization System based on Hierarchical Federated Learning is proposed in response to traditional indoor localization techniques' errors and privacy concerns.</li><li>The system utilizes Federated Learning (FL) with a Deep Neural Network (DNN) model for dynamic indoor localization, addressing privacy, bandwidth, and server reliability issues.</li><li>Experimental results show that FL-based approach achieves similar performance to a Centralized Model (CL) while ensuring data privacy, bandwidth efficiency, and server reliability.</li><li>The research suggests that this FL approach offers a secure and efficient solution for indoor localization, contributing to advancements in privacy-enhanced indoor positioning systems.</li></ul>

A Privacy-Preserving Indoor Localization System based on Hierarchical Federated Learning

Discover more