Deep learning's unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work presents an ethical federated learning model, incorporating all three measures simultaneously. Experiments on the Adult, Bank and Dutch datasets highlight the resulting ``empirical interplay" between accuracy, fairness, and privacy.

翻译：深层学习史无前例的成功引起了从有偏见的预测到数据隐私等若干伦理问题。 研究人员通过引入公平衡量标准、联合学习或差异隐私来解决这些问题。 首先,这项工作提出了一个道德的联邦学习模式,同时包含所有三项措施。 在成人、银行和荷兰数据集的实验凸显了准确性、公平性和隐私之间的“经验性互动 ” 。