# FairFed: Enabling Group Fairness in Federated Learning Source: [FairFed: Enabling Group Fairness in Federated Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:48:10.793Z Authors: Yahya H. Ezzeldin, Shen Yan, Chaoyang He, Emilio Ferrara, Salman Avestimehr Published: 2021-10-02T17:55:20.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on FACET (IID client shards)](): Object Detection; FACET (IID client shards); Overall mAP: 70.13. - [Sentiment Prediction on TweetEval (IID)](): Sentiment Prediction; TweetEval (IID); Accuracy: 53.1. - [Income Prediction on Adult (IID)](): Income Prediction; Adult (IID); Accuracy: 84.49. ## Original sources and implementation [Original paper]() [PDF]()