# Privacy-Preserving Cooperative Visible Light Positioning for Nonstationary Environment: A Federated Learning Perspective

arXiv: https://arxiv.org/abs/2303.06361 · HTML: https://mlsanity.com/paper/2303.06361/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 11 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [AI Benchmark: Running Deep Neural Networks on Android Smartphones](https://mlsanity.com/paper/1810.01109/) (2018)
- [Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation](https://mlsanity.com/paper/2007.11104/) (2020)

## Abstract (arXiv)

Visible light positioning (VLP) has drawn plenty of attention as a promising indoor positioning technique. However, in nonstationary environments, the performance of VLP is limited because of the highly time-varying channels. To improve the positioning accuracy and generalization capability in nonstationary environments, a cooperative VLP scheme based on federated learning (FL) is proposed in this paper. Exploiting the FL framework, a global model adaptive to environmental changes can be jointly trained by users without sharing private data of users. Moreover, a Cooperative Visible-light Positioning Network (CVPosNet) is proposed to accelerate the convergence rate and improve the positioning accuracy. Simulation results show that the proposed scheme outperforms the benchmark schemes, especially in nonstationary environments.