# Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks

arXiv: https://arxiv.org/abs/2503.19549 · HTML: https://mlsanity.com/paper/2503.19549/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 33 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data](https://mlsanity.com/paper/2005.11418/) (2020)
- [Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2009.13012/) (2020)
- [Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients](https://mlsanity.com/paper/2102.07053/) (2021)
- [Over-the-Air Federated Learning from Heterogeneous Data](https://mlsanity.com/paper/2009.12787/) (2020)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [6G Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2108.04973/) (2021)
- [On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond](https://mlsanity.com/paper/2206.05187/) (2022)
- [Robust Federated Learning with Noisy Communication](https://mlsanity.com/paper/1911.00251/) (2019)
- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [Over-The-Air Federated Learning: Status Quo, Open Challenges, and Future Directions](https://mlsanity.com/paper/2307.00974/) (2023)
- [Straggler-aware Distributed Learning: Communication Computation Latency Trade-off](https://mlsanity.com/paper/2004.04948/) (2020)
- [LASER: Linear Compression in Wireless Distributed Optimization](https://mlsanity.com/paper/2310.13033/) (2023)
- [A Demonstration of Over-the-Air Computation for Federated Edge Learning](https://mlsanity.com/paper/2209.09954/) (2022)
- [A Convergence Theory for Federated Average: Beyond Smoothness](https://mlsanity.com/paper/2211.01588/) (2022)
- [Distributed Convex Optimization "Over-the-Air" in Dynamic Environments](https://mlsanity.com/paper/2307.04913/) (2023)

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

- [Channel-Adaptive Robust Aggregation for Over-the-Air Federated Learning in Heterogeneous Networks](https://mlsanity.com/paper/2607.04218/) (2026)

## Abstract (arXiv)

In 6G wireless networks, Artificial Intelligence (AI)-driven applications demand the adoption of Federated Learning (FL) to enable efficient and privacy-preserving model training across distributed devices. Over-The-Air Federated Learning (OTA-FL) exploits the superposition property of multiple access channels, allowing edge users in 6G networks to efficiently share spectral resources and perform low-latency global model aggregation. However, these advantages come with challenges, as traditional OTA-FL techniques suffer due to the joint effects of Additive White Gaussian Noise (AWGN) at the server, fading, and both data and system heterogeneity at the participating edge devices. In this work, we propose the novel Noise Resilient Over-the-Air Federated Learning (NoROTA-FL) framework to jointly tackle these challenges in federated wireless networks. In NoROTA-FL, the local optimization problems find controlled inexact solutions, which manifests as an additional proximal constraint at the clients. This approach provides robustness against straggler-induced partial work, heterogeneity, noise, and fading. From a theoretical perspective, we leverage the zeroth- and first-order inexactness and establish convergence guarantees for non-convex optimization problems in the presence of heterogeneous data and varying system capabilities. Experimentally, we validate NoROTA-FL on real-world datasets, including FEMNIST, CIFAR10, and CIFAR100, demonstrating its robustness in noisy and heterogeneous environments. Compared to state-of-the-art baselines such as COTAF and FedProx, NoROTA-FL achieves significantly more stable convergence and higher accuracy, particularly in the presence of stragglers.