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.

翻译：尽管公平感知机器学习算法日益受到关注，但现有研究主要集中在集中式机器学习上，对分布式方法的探索尚不充分。联邦学习是一种分布式机器学习范式，其中客户端训练本地模型，并由服务器聚合这些模型以获得共享的全局模型。客户端间的数据异质性是联邦学习的常见特征，这可能会诱发或加剧对受敏感属性（如种族或性别）定义的非特权群体的歧视。本文提出FAIR-FATE：一种新颖的公平联邦学习算法，旨在通过一种公平感知的聚合方法在保持高实用性的同时实现群体公平性，该方法通过考虑客户端的公平性来计算全局模型。为实现这一目标，全局模型更新通过利用动量项估计公平模型更新来计算，该动量项有助于克服非公平梯度的振荡。据我们所知，这是机器学习领域首次尝试利用公平动量估计实现公平性的方法。在真实世界数据集上的实验结果表明，FAIR-FATE在不同数据异质性水平下均优于当前最先进的公平联邦学习算法。