Minimax Demographic Group Fairness in Federated Learning-猫眼课题宝

DOI：

10.1145/3531146.3533081

发表时间：

2022-01

期刊：

Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Afroditi Papadaki;Natalia Martínez;Martín Bertrán;G. Sapiro;Miguel R. D. Rodrigues

联邦学习是一种日益流行的范例，它使大量实体能够协作学习更好的模型。在这项工作中，我们研究了联邦学习场景中的极大极小群体公平性，在这种场景中，不同的参与实体在训练阶段可能只能访问人口群体的一个子集。我们正式分析了我们提出的群体公平目标与现有的联邦学习公平标准的不同之处，后者在参与者而不是人口统计群体中施加相似的表现。我们提供了一个优化算法- FedMinMax -来解决所提出的问题，证明它享有集中式学习算法的性能保证。我们通过实验将所提出的方法与其他最先进的方法在各种联邦学习设置中的组公平进行了比较，表明我们的方法具有竞争力或更好的性能。

Federated learning is an increasingly popular paradigm that enables a large number of entities to collaboratively learn better models. In this work, we study minimax group fairness in federated learning scenarios where different participating entities may only have access to a subset of the population groups during the training phase. We formally analyze how our proposed group fairness objective differs from existing federated learning fairness criteria that impose similar performance across participants instead of demographic groups. We provide an optimization algorithm – FedMinMax – for solving the proposed problem that provably enjoys the performance guarantees of centralized learning algorithms. We experimentally compare the proposed approach against other state-of-the-art methods in terms of group fairness in various federated learning setups, showing that our approach exhibits competitive or superior performance.

Minimax Demographic Group Fairness in Federated Learning

文献摘要

相似文献