# FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction

arXiv: https://arxiv.org/abs/2410.17269 · HTML: https://mlsanity.com/paper/2410.17269/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 70 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression](https://mlsanity.com/paper/2012.01536/) (2020)
- [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Explaining the Explainer: A First Theoretical Analysis of LIME](https://mlsanity.com/paper/2001.03447/) (2020)
- [Fairness-aware Agnostic Federated Learning](https://mlsanity.com/paper/2010.05057/) (2020)
- [Fairlearn: Assessing and Improving Fairness of AI Systems](https://mlsanity.com/paper/2303.16626/) (2023)
- [Towards Fairness-Aware Federated Learning](https://mlsanity.com/paper/2111.01872/) (2021)
- [Two Simple Ways to Learn Individual Fairness Metrics from Data](https://mlsanity.com/paper/2006.11439/) (2020)
- [Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning](https://mlsanity.com/paper/2108.08435/) (2021)
- [Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks](https://mlsanity.com/paper/2405.02225/) (2024)
- [Federated Offline Reinforcement Learning](https://mlsanity.com/paper/2206.05581/) (2022)
- [Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches](https://mlsanity.com/paper/2311.03417/) (2023)
- [Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis](https://mlsanity.com/paper/2407.11034/) (2024)
- ["And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation](https://mlsanity.com/paper/2009.02590/) (2020)
- [A synthetic data integration framework to leverage external summary-level information from heterogeneous populations](https://mlsanity.com/paper/2106.06835/) (2021)
- [FedScore: A privacy-preserving framework for federated scoring system development](https://mlsanity.com/paper/2303.00282/) (2023)
- [A Transformer-Based Deep Learning Approach for Fairly Predicting Post-Liver Transplant Risk Factors](https://mlsanity.com/paper/2304.02780/) (2023)
- [Federated and distributed learning applications for electronic health records and structured medical data: A scoping review](https://mlsanity.com/paper/2304.07310/) (2023)
- [Quantifying Health Inequalities Induced by Data and AI Models](https://mlsanity.com/paper/2205.01066/) (2022)

## Built on by (most cited first, 3 of 3)

- [BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agents](https://mlsanity.com/paper/2504.04855/) (2025)
- [Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity](https://mlsanity.com/paper/2509.17411/) (2025)
- [FedFACT: A Provable Framework for Controllable Group-Fairness Calibration in Federated Learning](https://mlsanity.com/paper/2506.03777/) (2025)

## Abstract (arXiv)

Objective: Mitigating algorithmic disparities is a critical challenge in healthcare research, where ensuring equity and fairness is paramount. While large-scale healthcare data exist across multiple institutions, cross-institutional collaborations often face privacy constraints, highlighting the need for privacy-preserving solutions that also promote fairness. Materials and Methods: In this study, we present Fair Federated Machine Learning (FairFML), a model-agnostic solution designed to reduce algorithmic bias in cross-institutional healthcare collaborations while preserving patient privacy. As a proof of concept, we validated FairFML using a real-world clinical case study focused on reducing gender disparities in cardiac arrest outcome prediction. Results: We demonstrate that the proposed FairFML framework enhances fairness in federated learning (FL) models without compromising predictive performance. Our findings show that FairFML improves model fairness by up to 65% compared to the centralized model, while maintaining performance comparable to both local and centralized models, as measured by receiver operating characteristic analysis. Discussion and Conclusion: FairFML offers a promising and flexible solution for FL collaborations, with its adaptability allowing seamless integration with various FL frameworks and models, from traditional statistical methods to deep learning techniques. This makes FairFML a robust approach for developing fairer FL models across diverse clinical and biomedical applications.