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

TL;DR

This paper introduces a federated learning-based indoor localization system that maintains high accuracy while enhancing privacy, bandwidth efficiency, and server reliability compared to traditional centralized methods.

Contribution

It presents a novel FL-based approach for indoor localization using DNNs, addressing privacy and efficiency issues inherent in centralized data collection.

Findings

FL achieves comparable accuracy to centralized models

The system enhances privacy and bandwidth efficiency

Experimental results validate the approach's effectiveness

Abstract

Location information serves as the fundamental element for numerous Internet of Things (IoT) applications. Traditional indoor localization techniques often produce significant errors and raise privacy concerns due to centralized data collection. In response, Machine Learning (ML) techniques offer promising solutions by capturing indoor environment variations. However, they typically require central data aggregation, leading to privacy, bandwidth, and server reliability issues. To overcome these challenges, in this paper, we propose a Federated Learning (FL)-based approach for dynamic indoor localization using a Deep Neural Network (DNN) model. Experimental results show that FL has the nearby performance to Centralized Model (CL) while keeping the data privacy, bandwidth efficiency and server reliability. This research demonstrates that our proposed FL approach provides a viable solution for privacy-enhanced indoor localization, paving the way for advancements in secure and efficient indoor localization systems.