Building fair machine learning models becomes more and more important. As many powerful models are built by collaboration among multiple parties, each holding some sensitive data, it is natural to explore the feasibility of training fair models in cross-silo federated learning so that fairness, privacy and collaboration can be fully respected simultaneously. However, it is a very challenging task, since it is far from trivial to accurately estimate the fairness of a model without knowing the private data of the participating parties. 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 cross-silo federated learning. We develop FedFair, a well-designed federated learning framework, which can successfully train a fair model with high performance without any data privacy infringement. Our extensive experiments on three real-world data sets demonstrate the excellent fair model training performance of our method.

本文提出了一种基于联邦估计方法的公平模型训练方案，可以在跨边界联邦学习中保护隐私和公平，实现高性能的公平模型训练，实验结果表明本方法的性能优异。

FedFair: 跨数据源联邦学习中的公平模型训练