# Federated Distillation based Indoor Localization for IoT Networks

arXiv: https://arxiv.org/abs/2205.11440 · HTML: https://mlsanity.com/paper/2205.11440/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 77 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data](https://mlsanity.com/paper/1811.11479/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Large scale distributed neural network training through online distillation](https://mlsanity.com/paper/1804.03235/) (2018)
- [Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data](https://mlsanity.com/paper/2008.06180/) (2020)
- [Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation](https://mlsanity.com/paper/1910.13067/) (2019)
- [Federated Knowledge Distillation](https://mlsanity.com/paper/2011.02367/) (2020)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data](https://mlsanity.com/paper/1907.02745/) (2019)
- [Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup](https://mlsanity.com/paper/2006.09801/) (2020)
- [Communication-Efficient Federated Distillation](https://mlsanity.com/paper/2012.00632/) (2020)
- [Novel Massive MIMO Channel Sounding Data Applied to Deep Learning-based Indoor Positioning](https://mlsanity.com/paper/1810.04126/) (2018)
- [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)
- [An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation](https://mlsanity.com/paper/2002.12597/) (2020)
- [A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges](https://mlsanity.com/paper/2202.11082/) (2022)
- [A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things](https://mlsanity.com/paper/2104.10501/) (2021)
- [DNN-based Localization from Channel Estimates: Feature Design and Experimental Results](https://mlsanity.com/paper/2004.00363/) (2020)
- [Federated Learning for Localization: A Privacy-Preserving Crowdsourcing Method](https://mlsanity.com/paper/2001.01911/) (2020)
- [Improving CSI-based Massive MIMO Indoor Positioning using Convolutional Neural Network](https://mlsanity.com/paper/2102.03130/) (2021)
- [Federated Learning-Based Localization with Heterogeneous Fingerprint Database](https://mlsanity.com/paper/2203.15388/) (2022)
- [Analysis of Knowledge Transfer in Kernel Regime](https://mlsanity.com/paper/2003.13438/) (2020)
- [RSSI Fingerprinting-based Localization Using Machine Learning in LoRa Networks](https://mlsanity.com/paper/2006.01278/) (2020)

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

- [Federated Distillation: A Survey](https://mlsanity.com/paper/2404.08564/) (2024)
- [Federated Learning based Hierarchical 3D Indoor Localization](https://mlsanity.com/paper/2303.00450/) (2023)
- [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)
- [ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization](https://mlsanity.com/paper/2603.19594/) (2026)

## Abstract (arXiv)

Federated distillation (FD) paradigm has been recently proposed as a promising alternative to federated learning (FL) especially in wireless sensor networks with limited communication resources. However, all state-of-the art FD algorithms are designed for only classification tasks and less attention has been given to regression tasks. In this work, we propose an FD framework that properly operates on regression learning problems. Afterwards, we present a use-case implementation by proposing an indoor localization system that shows a good trade-off communication load vs. accuracy compared to federated learning (FL) based indoor localization. With our proposed framework, we reduce the number of transmitted bits by up to 98%. Moreover, we show that the proposed framework is much more scalable than FL, thus more likely to cope with the expansion of wireless networks.