# Fair Federated Learning via Bounded Group Loss

arXiv: https://arxiv.org/abs/2203.10190 · HTML: https://mlsanity.com/paper/2203.10190/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 30 of 32)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018)
- [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019)
- [Minimax Pareto Fairness: A Multi Objective Perspective](https://mlsanity.com/paper/2011.01821/) (2020)
- [Federated Adversarial Domain Adaptation](https://mlsanity.com/paper/1911.02054/) (2019)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [FairBatch: Batch Selection for Model Fairness](https://mlsanity.com/paper/2012.01696/) (2020)
- [Fairness-aware Agnostic Federated Learning](https://mlsanity.com/paper/2010.05057/) (2020)
- [Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning](https://mlsanity.com/paper/2108.08435/) (2021)
- [Improving Fairness via Federated Learning](https://mlsanity.com/paper/2110.15545/) (2021)
- [Enforcing fairness in private federated learning via the modified method of differential multipliers](https://mlsanity.com/paper/2109.08604/) (2021)
- [Training Fair Models in Federated Learning without Data Privacy Infringement](https://mlsanity.com/paper/2109.05662/) (2021)
- [Minimax Demographic Group Fairness in Federated Learning](https://mlsanity.com/paper/2201.08304/) (2022)
- [Differentially Private SGDA for Minimax Problems](https://mlsanity.com/paper/2201.09046/) (2022)
- [GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning](https://mlsanity.com/paper/2108.02741/) (2021)
- [Efficient Algorithms for Federated Saddle Point Optimization](https://mlsanity.com/paper/2102.06333/) (2021)

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

- [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023)
- [Mitigating Group Bias in Federated Learning: Beyond Local Fairness](https://mlsanity.com/paper/2305.09931/) (2023)
- [Federated Unlearning: a Perspective of Stability and Fairness](https://mlsanity.com/paper/2402.01276/) (2024)
- [PraFFL: A Preference-Aware Scheme in Fair Federated Learning](https://mlsanity.com/paper/2404.08973/) (2024)
- [Fairness and Privacy in Federated Learning and Their Implications in Healthcare](https://mlsanity.com/paper/2308.07805/) (2023)
- [Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives](https://mlsanity.com/paper/2406.10884/) (2024)
- [Federated Fairness without Access to Sensitive Groups](https://mlsanity.com/paper/2402.14929/) (2024)
- [PFAttack: Stealthy Attack Bypassing Group Fairness in Federated Learning](https://mlsanity.com/paper/2410.06509/) (2024)
- [Global Group Fairness in Federated Learning via Function Tracking](https://mlsanity.com/paper/2503.15163/) (2025)
- [Anti-Matthew FL: Bridging the Performance Gap in Federated Learning to Counteract the Matthew Effect](https://mlsanity.com/paper/2309.16338/) (2023)
- [Distribution-Free Fair Federated Learning with Small Samples](https://mlsanity.com/paper/2402.16158/) (2024)
- [Towards Fairness in Provably Communication-Efficient Federated Recommender Systems](https://mlsanity.com/paper/2405.15788/) (2024)

## Abstract (arXiv)

Fair prediction across protected groups is an important constraint for many federated learning applications. However, prior work studying group fair federated learning lacks formal convergence or fairness guarantees. In this work we propose a general framework for provably fair federated learning. In particular, we explore and extend the notion of Bounded Group Loss as a theoretically-grounded approach for group fairness. Using this setup, we propose a scalable federated optimization method that optimizes the empirical risk under a number of group fairness constraints. We provide convergence guarantees for the method as well as fairness guarantees for the resulting solution. Empirically, we evaluate our method across common benchmarks from fair ML and federated learning, showing that it can provide both fairer and more accurate predictions than baseline approaches.