# FAIR-FATE: Fair Federated Learning with Momentum

arXiv: https://arxiv.org/abs/2209.13678 · HTML: https://mlsanity.com/paper/2209.13678/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 45 references that are in the map.
- First posted on arXiv 27 Sep 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Fairness in Machine Learning: A Survey](https://mlsanity.com/paper/2010.04053/) (2020)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Measurement and Fairness](https://mlsanity.com/paper/1912.05511/) (2019)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [Accelerating Federated Learning via Momentum Gradient Descent](https://mlsanity.com/paper/1910.03197/) (2019)
- [Mitigating Bias in Federated Learning](https://mlsanity.com/paper/2012.02447/) (2020)
- [Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions](https://mlsanity.com/paper/1901.10501/) (2019)
- [Equalized odds postprocessing under imperfect group information](https://mlsanity.com/paper/1906.03284/) (2019)
- [Fairness in Risk Assessment Instruments: Post-Processing to Achieve Counterfactual Equalized Odds](https://mlsanity.com/paper/2009.02841/) (2020)
- [Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach](https://mlsanity.com/paper/2008.01132/) (2020)
- [Improving Fairness via Federated Learning](https://mlsanity.com/paper/2110.15545/) (2021)
- [On the Moral Justification of Statistical Parity](https://mlsanity.com/paper/2011.02079/) (2020)
- [How to be Fair and Diverse?](https://mlsanity.com/paper/1610.07183/) (2016)
- [Removing biased data to improve fairness and accuracy](https://mlsanity.com/paper/2102.03054/) (2021)
- [Demon: Improved Neural Network Training with Momentum Decay](https://mlsanity.com/paper/1910.04952/) (2019)
- [Towards Multi-Objective Statistically Fair Federated Learning](https://mlsanity.com/paper/2201.09917/) (2022)

## Built on by (most cited first, 10 of 10)

- [PraFFL: A Preference-Aware Scheme in Fair Federated Learning](https://mlsanity.com/paper/2404.08973/) (2024)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning](https://mlsanity.com/paper/2402.07586/) (2024)
- [The Cost of Local and Global Fairness in Federated Learning](https://mlsanity.com/paper/2503.22762/) (2025)
- [Global Group Fairness in Federated Learning via Function Tracking](https://mlsanity.com/paper/2503.15163/) (2025)
- [A Multivocal Literature Review on Privacy and Fairness in Federated Learning](https://mlsanity.com/paper/2408.08666/) (2024)
- [Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning](https://mlsanity.com/paper/2409.09532/) (2024)
- [Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning](https://mlsanity.com/paper/2504.21775/) (2025)
- [Towards Group Fairness with Multiple Sensitive Attributes in Federated Foundation Models](https://mlsanity.com/paper/2506.18732/) (2025)
- [Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning](https://mlsanity.com/paper/2605.08616/) (2026)

## Abstract (arXiv)

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decentralized form of machine learning where clients train local models with a server aggregating them to obtain a shared global model. Data heterogeneity amongst clients is a common characteristic of Federated Learning, which may induce or exacerbate discrimination of unprivileged groups defined by sensitive attributes such as race or gender. In this work we propose FAIR-FATE: a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients. To achieve that, the global model update is computed by estimating a fair model update using a Momentum term that helps to overcome the oscillations of non-fair gradients. To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate. Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms under different levels of data heterogeneity.