# FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis

arXiv: https://arxiv.org/abs/2403.13247 · HTML: https://mlsanity.com/paper/2403.13247/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 33 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 26 of 26)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication](https://mlsanity.com/paper/1902.00340/) (2019)
- [Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization](https://mlsanity.com/paper/1709.08765/) (2017)
- [Distributed Stochastic Gradient Tracking Methods](https://mlsanity.com/paper/1805.11454/) (2018)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2102.04761/) (2021)
- [Design and Analysis of Uplink and Downlink Communications for Federated Learning](https://mlsanity.com/paper/2012.04057/) (2020)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [An Exact Quantized Decentralized Gradient Descent Algorithm](https://mlsanity.com/paper/1806.11536/) (2018)
- [High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning](https://mlsanity.com/paper/1910.03865/) (2019)
- [Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach](https://mlsanity.com/paper/2209.00778/) (2022)
- [Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2209.15505/) (2022)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [A Survey of Federated Learning for Connected and Automated Vehicles](https://mlsanity.com/paper/2303.10677/) (2023)
- [On the Convergence of Decentralized Federated Learning Under Imperfect Information Sharing](https://mlsanity.com/paper/2303.10695/) (2023)
- [Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data](https://mlsanity.com/paper/2305.04792/) (2023)
- [Communication-Efficient Variance-Reduced Decentralized Stochastic Optimization over Time-Varying Directed Graphs](https://mlsanity.com/paper/2101.09583/) (2021)
- [Communication-efficient Decentralized Local SGD over Undirected Networks](https://mlsanity.com/paper/2011.03255/) (2020)
- [Decentralized Optimization On Time-Varying Directed Graphs Under Communication Constraints](https://mlsanity.com/paper/2005.13189/) (2020)
- [Improved Convergence Rate for a Distributed Two-Time-Scale Gradient Method under Random Quantization](https://mlsanity.com/paper/2105.14089/) (2021)
- [Distributed Optimization over Time-varying Graphs with Imperfect Sharing of Information](https://mlsanity.com/paper/2106.08469/) (2021)
- [Improved Convergence Analysis and SNR Control Strategies for Federated Learning in the Presence of Noise](https://mlsanity.com/paper/2307.07406/) (2023)
- [FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication](https://mlsanity.com/paper/2310.07048/) (2023)
- [Almost Sure Convergence of Distributed Optimization with Imperfect Information Sharing](https://mlsanity.com/paper/2210.05897/) (2022)

## Built on by (most cited first, 2 of 2)

- [Unified High-Probability Analysis of Stochastic Variance-Reduced Estimation](https://mlsanity.com/paper/2605.15388/) (2026)
- [Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data](https://mlsanity.com/paper/2505.20485/) (2025)

## Abstract (arXiv)

A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm (FedNMUT) is proposed that is tailored to function efficiently in the presence of noisy communication channels that reflect imperfect information exchange. This algorithm uses gradient tracking to minimize the impact of data heterogeneity while minimizing communication overhead. The proposed algorithm incorporates noise into its parameters to mimic the conditions of noisy communication channels, thereby enabling consensus among clients through a communication graph topology in such challenging environments. FedNMUT prioritizes parameter sharing and noise incorporation to increase the resilience of decentralized learning systems against noisy communications. Theoretical results for the smooth non-convex objective function are provided by us, and it is shown that the ε-stationary solution is achieved by our algorithm at the rate of Oleft(frac1√Tright), where T is the total number of communication rounds. Additionally, via empirical validation, we demonstrated that the performance of FedNMUT is superior to the existing state-of-the-art methods and conventional parameter-mixing approaches in dealing with imperfect information sharing. This proves the capability of the proposed algorithm to counteract the negative effects of communication noise in a decentralized learning framework.