# Robust Federated Learning with Noisy Communication

arXiv: https://arxiv.org/abs/1911.00251 · HTML: https://mlsanity.com/paper/1911.00251/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2019.

## Built on by (most cited first, 30 of 34)

- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [Decentralized Federated Learning: Balancing Communication and Computing Costs](https://mlsanity.com/paper/2107.12048/) (2021)
- [An Experimental Study of Byzantine-Robust Aggregation Schemes in Federated Learning](https://mlsanity.com/paper/2302.07173/) (2023)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices](https://mlsanity.com/paper/2402.15903/) (2024)
- [Federated learning: Applications, challenges and future directions](https://mlsanity.com/paper/2205.09513/) (2022)
- [Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point](https://mlsanity.com/paper/2203.13950/) (2022)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Federated Learning over Noisy Channels: Convergence Analysis and Design Examples](https://mlsanity.com/paper/2101.02198/) (2021)
- [A Hybrid Architecture for Federated and Centralized Learning](https://mlsanity.com/paper/2105.03288/) (2021)
- [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023)
- [Energy and Spectrum Efficient Federated Learning via High-Precision Over-the-Air Computation](https://mlsanity.com/paper/2208.07237/) (2022)
- [Communication-Efficient Design for Quantized Decentralized Federated Learning](https://mlsanity.com/paper/2303.08423/) (2023)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms](https://mlsanity.com/paper/2207.02337/) (2022)
- [Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise](https://mlsanity.com/paper/2307.07406/) (2023)
- [FedAST: Federated Asynchronous Simultaneous Training](https://mlsanity.com/paper/2406.00302/) (2024)
- [Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design](https://mlsanity.com/paper/2407.13703/) (2024)
- [Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning](https://mlsanity.com/paper/2101.08696/) (2021)
- [SWAP: Sparse Entropic Wasserstein Regression for Robust Network Pruning](https://mlsanity.com/paper/2310.04918/) (2023)
- [How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels](https://mlsanity.com/paper/2310.16652/) (2023)
- [Adversarial Robustness in Distributed Quantum Machine Learning](https://mlsanity.com/paper/2508.11848/) (2025)
- [TabVFL: Improving Latent Representation in Vertical Federated Learning](https://mlsanity.com/paper/2404.17990/) (2024)
- [Noise-Robust and Resource-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2409.13451/) (2024)
- [Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks](https://mlsanity.com/paper/2503.19549/) (2025)
- [FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems](https://mlsanity.com/paper/2203.07276/) (2022)
- [Revisiting Communication-Efficient Federated Learning with Balanced Global and Local Updates](https://mlsanity.com/paper/2205.01470/) (2022)
- [Dependable Distributed Training of Compressed Machine Learning Models](https://mlsanity.com/paper/2402.14346/) (2024)
- [Strategic Federated Learning: Application to Smart Meter Data Clustering](https://mlsanity.com/paper/2408.02384/) (2024)
- [Communication-Efficient Federated Learning over Wireless Channels via Gradient Sketching](https://mlsanity.com/paper/2410.23424/) (2024)

## Abstract (arXiv)

Federated learning is a communication-efficient training process that alternates between local training at the edge devices and averaging the updated local model at the central server. Nevertheless, it is impractical to achieve a perfect acquisition of the local models in wireless communication due to noise, which also brings serious effects on federated learning. To tackle this challenge, we propose a robust design for federated learning to alleviate the effects of noise in this paper. Considering noise in the two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and the worst-case model. Due to the non-convexity of the problem, a regularization for the loss function approximation method is proposed to make it tractable. Regarding the worst-case model, we develop a feasible training scheme which utilizes the sampling-based successive convex approximation algorithm to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function are demonstrated via simulations for the proposed designs.