# Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation

arXiv: https://arxiv.org/abs/2404.03088 · HTML: https://mlsanity.com/paper/2404.03088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 13 of 13)

- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning](https://mlsanity.com/paper/2009.01974/) (2020)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [Bayesian Federated Learning: A Survey](https://mlsanity.com/paper/2304.13267/) (2023)
- [Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocation](https://mlsanity.com/paper/2002.00802/) (2020)
- [Communication-Efficient Federated Learning over MIMO Multiple Access Channels](https://mlsanity.com/paper/2206.05723/) (2022)
- [FedRDF: A Robust and Dynamic Aggregation Function against Poisoning Attacks in Federated Learning](https://mlsanity.com/paper/2402.10082/) (2024)
- [Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks](https://mlsanity.com/paper/2208.10279/) (2022)
- [A Secure and Robust Approach for Distance-Based Mutual Positioning of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2305.12021/) (2023)
- [A Reliable and Resilient Framework for Multi-UAV Mutual Localization](https://mlsanity.com/paper/2309.04270/) (2023)

## Abstract (arXiv)

Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation.