# Robust Networked Federated Learning for Localization

arXiv: https://arxiv.org/abs/2308.16737 · HTML: https://mlsanity.com/paper/2308.16737/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 31 Aug 2023.

## Builds on (most cited first, 4 of 4)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima](https://mlsanity.com/paper/2003.02818/) (2020)
- [Local Strong Convexity of Source Localization and Error Bound for Target Tracking under Time-of-Arrival Measurements](https://mlsanity.com/paper/2112.11045/) (2021)
- [Robust Target Localization Based on Squared Range Iterative Reweighted Least Squares](https://mlsanity.com/paper/1802.05235/) (2018)

## Built on by (most cited first, 1 of 1)

- [Federated Smoothing ADMM for Localization](https://mlsanity.com/paper/2503.09497/) (2025)

## Abstract (arXiv)

This paper addresses the problem of localization, which is inherently non-convex and non-smooth in a federated setting where the data is distributed across a multitude of devices. Due to the decentralized nature of federated environments, distributed learning becomes essential for scalability and adaptability. Moreover, these environments are often plagued by outlier data, which presents substantial challenges to conventional methods, particularly in maintaining estimation accuracy and ensuring algorithm convergence. To mitigate these challenges, we propose a method that adopts an L1-norm robust formulation within a distributed sub-gradient framework, explicitly designed to handle these obstacles. Our approach addresses the problem in its original form, without resorting to iterative simplifications or approximations, resulting in enhanced computational efficiency and improved estimation accuracy. We demonstrate that our method converges to a stationary point, highlighting its effectiveness and reliability. Through numerical simulations, we confirm the superior performance of our approach, notably in outlier-rich environments, which surpasses existing state-of-the-art localization methods.