# Noise-Robust and Resource-Efficient ADMM-based Federated Learning

arXiv: https://arxiv.org/abs/2409.13451 · HTML: https://mlsanity.com/paper/2409.13451/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 64 references that are in the map.
- First posted on arXiv 20 Sep 2024.

## Builds on (most cited first, 30 of 31)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [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)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Federated Learning over Wireless Fading Channels](https://mlsanity.com/paper/1907.09769/) (2019)
- [Mitigating Sybils in Federated Learning Poisoning](https://mlsanity.com/paper/1808.04866/) (2018)
- [FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learning](https://mlsanity.com/paper/2208.12268/) (2022)
- [EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning](https://mlsanity.com/paper/2108.08842/) (2021)
- [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021)
- [Design and Analysis of Uplink and Downlink Communications for Federated Learning](https://mlsanity.com/paper/2012.04057/) (2020)
- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [Robust Federated Learning with Noisy Communication](https://mlsanity.com/paper/1911.00251/) (2019)
- [Federated Learning for Ultra-Reliable Low-Latency V2V Communications](https://mlsanity.com/paper/1805.09253/) (2018)
- [Convergence of Federated Learning over a Noisy Downlink](https://mlsanity.com/paper/2008.11141/) (2020)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [Communication-Efficient Federated Learning via Quantized Compressed Sensing](https://mlsanity.com/paper/2111.15071/) (2021)
- [Communication-Efficient Online Federated Learning Framework for Nonlinear Regression](https://mlsanity.com/paper/2110.06556/) (2021)
- [Beyond ADMM: A Unified Client-variance-reduced Adaptive Federated Learning Framework](https://mlsanity.com/paper/2212.01519/) (2022)
- [Federated Learning over Noisy Channels: Convergence Analysis and Design Examples](https://mlsanity.com/paper/2101.02198/) (2021)
- [Communication-Efficient Design for Quantized Decentralized Federated Learning](https://mlsanity.com/paper/2303.08423/) (2023)
- [Personalized Graph Federated Learning with Differential Privacy](https://mlsanity.com/paper/2306.06399/) (2023)
- [Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing](https://mlsanity.com/paper/2403.13108/) (2024)
- [Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Design](https://mlsanity.com/paper/2407.13703/) (2024)
- [Privacy-Preserving Distributed Nonnegative Matrix Factorization](https://mlsanity.com/paper/2403.18326/) (2024)
- [On the Asymptotic Bias of the Diffusion-Based Distributed Pareto Optimization](https://mlsanity.com/paper/1511.04428/) (2015)

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

- [Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing](https://mlsanity.com/paper/2602.18396/) (2026)

## Abstract (arXiv)

Federated learning (FL) leverages client-server communications to train global models on decentralized data. However, communication noise or errors can impair model accuracy. To address this problem, we propose a novel FL algorithm that enhances robustness against communication noise while also reducing communication load. We derive the proposed algorithm through solving the weighted least-squares (WLS) regression problem as an illustrative example. We first frame WLS regression as a distributed convex optimization problem over a federated network employing random scheduling for improved communication efficiency. We then apply the alternating direction method of multipliers (ADMM) to iteratively solve this problem. To counteract the detrimental effects of cumulative communication noise, we introduce a key modification by eliminating the dual variable and implementing a new local model update at each participating client. This subtle yet effective change results in using a single noisy global model update at each client instead of two, improving robustness against additive communication noise. Furthermore, we incorporate another modification enabling clients to continue local updates even when not selected by the server, leading to substantial performance improvements. Our theoretical analysis confirms the convergence of our algorithm in both mean and the mean-square senses, even when the server communicates with a random subset of clients over noisy links at each iteration. Numerical results validate the effectiveness of our proposed algorithm and corroborate our theoretical findings.