Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy while ensuring good utility poses a significant challenge. The interplay between these three factors of trustworthiness is frequently underestimated and remains insufficiently explored. Consequently, many efforts focus on ensuring only two of these factors, neglecting one in the process. The decentralization of the datasets and the variations in distributions among the clients exacerbate the complexity of achieving this ethical trade-off in the context of Federated Learning (FL). For the first time in FL literature, we address these three factors of trustworthiness. We introduce PUFFLE, a high-level parameterised approach that can help in the exploration of the balance between utility, privacy, and fairness in FL scenarios. We prove that PUFFLE can be effective across diverse datasets, models, and data distributions, reducing the model unfairness up to 75%, with a maximum reduction in the utility of 17% in the worst-case scenario, while maintaining strict privacy guarantees during the FL training.

翻译：训练和部署同时遵循公平性与隐私原则，同时确保良好效用的机器学习模型是一项重大挑战。这三个可信赖因素之间的相互作用常常被低估，且仍未得到充分探索。因此，许多工作仅侧重于确保其中两个因素，而在此过程中忽略了第三个因素。数据集的去中心化以及客户端间分布差异加剧了在联邦学习（FL）背景下实现这一伦理权衡的复杂性。在FL文献中，我们首次同时处理这三个可信赖因素。我们提出了PUFFLE，这是一种高层参数化方法，有助于探索FL场景中效用、隐私与公平性之间的平衡。我们证明PUFFLE能够在不同数据集、模型和数据分布中有效应用，在联邦学习训练期间保持严格隐私保证的前提下，将模型不公平性降低高达75%，在最坏情况下效用最多仅降低17%。