Federated Learning (FL) has emerged as a result of data ownership and privacy
concerns to prevent data from being shared between multiple parties included in
a training procedure. Although issues, such as privacy, have gained significant
attention in this domain, not much attention has been given to satisfying
statistical fairness measures in the FL setting. With this goal in mind, we
conduct studies to show that FL is able to satisfy different fairness metrics
under different data regimes consisting of different types of clients. More
specifically, uncooperative or adversarial clients might contaminate the global
FL model by injecting biased or poisoned models due to existing biases in their
training datasets. Those biases might be a result of imbalanced training set
(Zhang and Zhou 2019), historical biases (Mehrabi et al. 2021a), or poisoned
data-points from data poisoning attacks against fairness (Mehrabi et al. 2021b;
Solans, Biggio, and Castillo 2020). Thus, we propose a new FL framework that is
able to satisfy multiple objectives including various statistical fairness
metrics. Through experimentation, we then show the effectiveness of this method
comparing it with various baselines, its ability in satisfying different
objectives collectively and individually, and its ability in identifying
uncooperative or adversarial clients and down-weighing their effect

本文针对联邦学习领域中的数据隐私和公平性问题，提出了一种能够满足不同统计公平度量的新方法，并进行实验验证其在满足多个目标方面的有效性和鲁棒性。