# Federated Learning based Hierarchical 3D Indoor Localization

arXiv: https://arxiv.org/abs/2303.00450 · HTML: https://mlsanity.com/paper/2303.00450/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Cooperative Learning via Federated Distillation over Fading Channels](https://mlsanity.com/paper/2002.01337/) (2020)
- [A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting](https://mlsanity.com/paper/1712.01990/) (2017)
- [A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges](https://mlsanity.com/paper/2202.11082/) (2022)
- [Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks](https://mlsanity.com/paper/2112.12478/) (2021)
- [Federated Distillation based Indoor Localization for IoT Networks](https://mlsanity.com/paper/2205.11440/) (2022)
- [Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach](https://mlsanity.com/paper/2108.02257/) (2021)

## Built on by (most cited first, 6 of 6)

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks](https://mlsanity.com/paper/2405.11079/) (2024)
- [MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion](https://mlsanity.com/paper/2411.17781/) (2024)
- [A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments](https://mlsanity.com/paper/2405.09960/) (2024)
- [A Privacy-Preserving Indoor Localization System based on Hierarchical Federated Learning](https://mlsanity.com/paper/2507.01581/) (2025)
- [Interpretable Physics Informed WiFi Indoor Localization: Learning an Effective Access Point Geometry and Using It to Prune](https://mlsanity.com/paper/2609.32539/) (2026)

## Abstract (arXiv)

The proliferation of connected devices in indoor environments opens the floor to a myriad of indoor applications with positioning services as key enablers. However, as privacy issues and resource constraints arise, it becomes more challenging to design accurate positioning systems as required by most applications. To overcome the latter challenges, we present in this paper, a federated learning (FL) framework for hierarchical 3D indoor localization using a deep neural network. Indeed, we firstly shed light on the prominence of exploiting the hierarchy between floors and buildings in a multi-building and multi-floor indoor environment. Then, we propose an FL framework to train the designed hierarchical model. The performance evaluation shows that by adopting a hierarchical learning scheme, we can improve the localization accuracy by up to 24.06% compared to the non-hierarchical approach. We also obtain a building and floor prediction accuracy of 99.90% and 94.87% respectively. With the proposed FL framework, we can achieve a near-performance characteristic as of the central training with an increase of only 7.69% in the localization error. Moreover, the conducted scalability study reveals that the FL system accuracy is improved when more devices join the training.