Training fair machine learning models becomes more and more important. As many powerful models are trained by collaboration among multiple parties, each holding some sensitive data, it is natural to explore the feasibility of training fair models in federated learning so that the fairness of trained models, the data privacy of clients, and the collaboration between clients can be fully respected simultaneously. However, the task of training fair models in federated learning is challenging, since it is far from trivial to estimate the fairness of a model without knowing the private data of the participating parties, which is often constrained by privacy requirements in federated learning. In this paper, we first propose a federated estimation method to accurately estimate the fairness of a model without infringing the data privacy of any party. Then, we use the fairness estimation to formulate a novel problem of training fair models in federated learning. We develop FedFair, a well-designed federated learning framework, which can successfully train a fair model with high performance without data privacy infringement. Our extensive experiments on three real-world data sets demonstrate the excellent fair model training performance of our method.

翻译：训练公平的机器学习模型正变得越来越重要。由于许多强大模型是通过多方协作训练的，各方均持有部分敏感数据，因此自然需要探索在联邦学习中训练公平模型的可行性，从而同时充分尊重训练模型的公平性、客户端的数据隐私以及客户端间的协作。然而，联邦学习中的公平模型训练任务具有挑战性，因为在不知道参与方私有数据的情况下评估模型的公平性远非易事，而这通常受到联邦学习中隐私要求的限制。本文首先提出一种联邦估计方法，该方法能在不侵犯任何一方数据隐私的前提下准确估计模型的公平性。随后，我们利用公平性估计构建了联邦学习中训练公平模型的新问题。我们开发了FedFair——一个精心设计的联邦学习框架，该框架能够在无数据隐私侵犯的情况下成功训练出具有高性能的公平模型。我们在三个真实数据集上的大量实验证明了该方法在公平模型训练方面的优异性能。