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.
翻译:建立公平的机器学习模式变得越来越重要。由于许多强大的模型是通过多方合作建立的,每个方都持有一些敏感数据,因此自然要探索在跨筒仓联合学习中培训公平模型的可行性,以便同时充分尊重公平、隐私和合作。然而,这是一项非常艰巨的任务,因为在不了解参与方的私人数据的情况下准确估计模型的公平性远非微不足道。在本文件中,我们首先提出一个联合估算方法,以准确估计模型的公平性,同时又不侵犯任何方的数据隐私。然后,我们利用公平估算来制定跨筒仓联合学习中培训公平模型的新问题。我们开发了一个设计完善的联邦法伊尔,这是一个设计良好的联邦学习框架,它能够成功地培训出一个业绩高且不侵犯数据隐私的公平模型。我们在三个真实世界数据集上进行的广泛实验,展示了我们方法的极好的公平模式培训表现。