# FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices

arXiv: https://arxiv.org/abs/2307.01780 · HTML: https://mlsanity.com/paper/2307.01780/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization](https://mlsanity.com/paper/2205.08069/) (2022)
- [VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization](https://mlsanity.com/paper/2302.09443/) (2023)
- [Simple and Effective Augmentation Methods for CSI Based Indoor Localization](https://mlsanity.com/paper/2211.10790/) (2022)
- [Federated Learning-Based Localization with Heterogeneous Fingerprint Database](https://mlsanity.com/paper/2203.15388/) (2022)
- [Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization](https://mlsanity.com/paper/2202.01980/) (2022)
- [Towards Robust Crowdsourcing-Based Localization: A Fingerprinting Accuracy Indicator Enhanced Wireless/Magnetic/Inertial Integration Approach](https://mlsanity.com/paper/2004.04688/) (2020)

## Built on by (most cited first, 9 of 9)

- [STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization](https://mlsanity.com/paper/2312.10312/) (2023)
- [CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization](https://mlsanity.com/paper/2311.06361/) (2023)
- [SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks](https://mlsanity.com/paper/2407.11091/) (2024)
- [AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems](https://mlsanity.com/paper/2408.04797/) (2024)
- [Holistic Evaluation Metrics: Use Case Sensitive Evaluation Metrics for Federated Learning](https://mlsanity.com/paper/2405.02360/) (2024)
- [DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones](https://mlsanity.com/paper/2506.15554/) (2025)
- [Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates](https://mlsanity.com/paper/2506.15559/) (2025)
- [Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization](https://mlsanity.com/paper/2511.17829/) (2025)
- [ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization](https://mlsanity.com/paper/2603.19594/) (2026)

## Abstract (arXiv)

Indoor localization plays a vital role in applications such as emergency response, warehouse management, and augmented reality experiences. By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments. However, achieving accurate indoor localization can be challenging due to heterogeneity in the hardware and software stacks of mobile devices, which can result in inconsistent and inaccurate location estimates. Traditional ML models also heavily rely on initial training data, making them vulnerable to degradation in performance with dynamic changes across indoor environments. To address the challenges due to device heterogeneity and lack of adaptivity, we propose a novel embedded ML framework called FedHIL. Our framework combines indoor localization and federated learning (FL) to improve indoor localization accuracy in device-heterogeneous environments while also preserving user data privacy. FedHIL integrates a domain-specific selective weight adjustment approach to preserve the ML model's performance for indoor localization during FL, even in the presence of extremely noisy data. Experimental evaluations in diverse real-world indoor environments and with heterogeneous mobile devices show that FedHIL outperforms state-of-the-art FL and non-FL indoor localization frameworks. FedHIL is able to achieve 1.62x better localization accuracy on average than the best performing FL-based indoor localization framework from prior work.