# Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data

arXiv: https://arxiv.org/abs/2305.04792 · HTML: https://mlsanity.com/paper/2305.04792/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication](https://mlsanity.com/paper/1902.00340/) (2019)
- [Stochastic Gradient Push for Distributed Deep Learning](https://mlsanity.com/paper/1811.10792/) (2018)
- [D2: Decentralized Training over Decentralized Data](https://mlsanity.com/paper/1803.07068/) (2018)
- [NEXT: In-Network Nonconvex Optimization](https://mlsanity.com/paper/1602.00591/) (2016)
- [Distributed Stochastic Gradient Tracking Methods](https://mlsanity.com/paper/1805.11454/) (2018)
- [Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2102.04761/) (2021)
- [Evolving Normalization-Activation Layers](https://mlsanity.com/paper/2004.02967/) (2020)
- [RelaySum for Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2110.04175/) (2021)
- [Cross-Gradient Aggregation for Decentralized Learning from Non-IID data](https://mlsanity.com/paper/2103.02051/) (2021)
- [Momentum Tracking: Momentum Acceleration for Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2209.15505/) (2022)
- [Decentralized Deep Learning using Momentum-Accelerated Consensus](https://mlsanity.com/paper/2010.11166/) (2020)
- [Neighborhood Gradient Clustering: An Efficient Decentralized Learning Method for Non-IID Data Distributions](https://mlsanity.com/paper/2209.14390/) (2022)

## Built on by (most cited first, 12 of 12)

- [Scalable Decentralized Learning with Teleportation](https://mlsanity.com/paper/2501.15259/) (2025)
- [FedNMUT -- Federated Noisy Model Update Tracking Convergence Analysis](https://mlsanity.com/paper/2403.13247/) (2024)
- [PDSL: Privacy-Preserved Decentralized Stochastic Learning with Heterogeneous Data Distribution](https://mlsanity.com/paper/2503.23726/) (2025)
- [Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data](https://mlsanity.com/paper/2403.03292/) (2024)
- [ROSS: RObust decentralized Stochastic learning based on Shapley values](https://mlsanity.com/paper/2411.00365/) (2024)
- [Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning](https://mlsanity.com/paper/2605.19969/) (2026)
- [Cross-feature Contrastive Loss for Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2310.15890/) (2023)
- [SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous Data](https://mlsanity.com/paper/2405.13961/) (2024)
- [Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning](https://mlsanity.com/paper/2501.04453/) (2025)
- [Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum](https://mlsanity.com/paper/2504.12742/) (2025)
- [DECA: Decentralizing Block-Wise Adam for Efficient LLM Full-Parameter Fine-Tuning on Non-IID Data](https://mlsanity.com/paper/2606.03209/) (2026)
- [DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data](https://mlsanity.com/paper/2606.04399/) (2026)

## Abstract (arXiv)

Decentralized learning enables the training of deep learning models over large distributed datasets generated at different locations, without the need for a central server. However, in practical scenarios, the data distribution across these devices can be significantly different, leading to a degradation in model performance. In this paper, we focus on designing a decentralized learning algorithm that is less susceptible to variations in data distribution across devices. We propose Global Update Tracking (GUT), a novel tracking-based method that aims to mitigate the impact of heterogeneous data in decentralized learning without introducing any communication overhead. We demonstrate the effectiveness of the proposed technique through an exhaustive set of experiments on various Computer Vision datasets (CIFAR-10, CIFAR-100, Fashion MNIST, and ImageNette), model architectures, and network topologies. Our experiments show that the proposed method achieves state-of-the-art performance for decentralized learning on heterogeneous data via a 1-6% improvement in test accuracy compared to other existing techniques.