# FairFed: Enabling Group Fairness in Federated Learning

arXiv: https://arxiv.org/abs/2110.00857 · HTML: https://mlsanity.com/paper/2110.00857/

## Key facts

- Cited by 118 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 40 references that are in the map.
- First posted on arXiv 2 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [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)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [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)
- [Mitigating Unwanted Biases with Adversarial Learning](https://mlsanity.com/paper/1801.07593/) (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)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [FairBatch: Batch Selection for Model Fairness](https://mlsanity.com/paper/2012.01696/) (2020)
- [Collaborative Fairness in Federated Learning](https://mlsanity.com/paper/2008.12161/) (2020)
- [Fairness-aware Agnostic Federated Learning](https://mlsanity.com/paper/2010.05057/) (2020)
- [Bias Mitigation Post-processing for Individual and Group Fairness](https://mlsanity.com/paper/1812.06135/) (2018)
- [Federated Learning with Fair Averaging](https://mlsanity.com/paper/2104.14937/) (2021)
- [Mitigating Bias in Federated Learning](https://mlsanity.com/paper/2012.02447/) (2020)
- [Enforcing fairness in private federated learning via the modified method of differential multipliers](https://mlsanity.com/paper/2109.08604/) (2021)
- [TILES-2018, a longitudinal physiologic and behavioral data set of hospital workers](https://mlsanity.com/paper/2003.08474/) (2020)

## Built on by (most cited first, 30 of 118)

- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Improving Fairness via Federated Learning](https://mlsanity.com/paper/2110.15545/) (2021)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity](https://mlsanity.com/paper/2405.16585/) (2024)
- [Privacy and Fairness in Federated Learning: on the Perspective of Trade-off](https://mlsanity.com/paper/2306.14123/) (2023)
- [Minimax Demographic Group Fairness in Federated Learning](https://mlsanity.com/paper/2201.08304/) (2022)
- [Local Stochastic Bilevel Optimization with Momentum-Based Variance Reduction](https://mlsanity.com/paper/2205.01608/) (2022)
- [Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods](https://mlsanity.com/paper/2305.11386/) (2023)
- [Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications](https://mlsanity.com/paper/2207.11812/) (2022)
- [PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning](https://mlsanity.com/paper/2205.11584/) (2022)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Fair Federated Learning via Bounded Group Loss](https://mlsanity.com/paper/2203.10190/) (2022)
- [Bias Propagation in Federated Learning](https://mlsanity.com/paper/2309.02160/) (2023)
- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion](https://mlsanity.com/paper/2410.20380/) (2024)
- [FAIR-FATE: Fair Federated Learning with Momentum](https://mlsanity.com/paper/2209.13678/) (2022)
- [Mitigating Group Bias in Federated Learning: Beyond Local Fairness](https://mlsanity.com/paper/2305.09931/) (2023)
- [Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition](https://mlsanity.com/paper/2307.11333/) (2023)
- [Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study](https://mlsanity.com/paper/2403.17287/) (2024)
- [Federated Analytics: A survey](https://mlsanity.com/paper/2302.01326/) (2023)
- [An Empirical Analysis of Fairness Notions under Differential Privacy](https://mlsanity.com/paper/2302.02910/) (2023)
- [Towards Fair Graph Federated Learning via Incentive Mechanisms](https://mlsanity.com/paper/2312.13306/) (2023)
- [FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing](https://mlsanity.com/paper/2402.08578/) (2024)
- [Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity](https://mlsanity.com/paper/2408.04023/) (2024)
- [Federated Unlearning: a Perspective of Stability and Fairness](https://mlsanity.com/paper/2402.01276/) (2024)
- [S2FGL: Spatial Spectral Federated Graph Learning](https://mlsanity.com/paper/2507.02409/) (2025)
- [Reward Systems for Trustworthy Medical Federated Learning](https://mlsanity.com/paper/2205.00470/) (2022)

## Abstract (arXiv)

Training ML models which are fair across different demographic groups is of critical importance due to the increased integration of ML in crucial decision-making scenarios such as healthcare and recruitment. Federated learning has been viewed as a promising solution for collaboratively training machine learning models among multiple parties while maintaining the privacy of their local data. However, federated learning also poses new challenges in mitigating the potential bias against certain populations (e.g., demographic groups), as this typically requires centralized access to the sensitive information (e.g., race, gender) of each datapoint. Motivated by the importance and challenges of group fairness in federated learning, in this work, we propose FairFed, a novel algorithm for fairness-aware aggregation to enhance group fairness in federated learning. Our proposed approach is server-side and agnostic to the applied local debiasing thus allowing for flexible use of different local debiasing methods across clients. We evaluate FairFed empirically versus common baselines for fair ML and federated learning, and demonstrate that it provides fairer models particularly under highly heterogeneous data distributions across clients. We also demonstrate the benefits of FairFed in scenarios involving naturally distributed real-life data collected from different geographical locations or departments within an organization.