Data augmentation is widely applied and has shown its benefits in different
machine learning tasks. However, as recently observed in some downstream tasks,
data augmentation may introduce an unfair impact on classifications. While it
can improve the performance of some classes, it can actually be detrimental for
other classes, which can be problematic in some application domains. In this
paper, to counteract this phenomenon, we propose a FAir Classification approach
with a Two-player game (FACT). We first formulate the training of a classifier
with data augmentation as a fair optimization problem, which can be further
written as an adversarial two-player game. Following this formulation, we
propose a novel multiplicative weight optimization algorithm, for which we
theoretically prove that it can converge to a solution that is fair over
classes. Interestingly, our formulation also reveals that this fairness issue
over classes is not due to data augmentation only, but is in fact a general
phenomenon. Our empirical experiments demonstrate that the performance of our
learned classifiers is indeed more fairly distributed over classes in five
datasets, with only limited impact on the average accuracy.

通过提出一种名为 FACT 的公平分类方法，本文针对数据增强在分类任务中带来的不公平影响问题进行了研究。通过将使用数据增强的分类器训练问题形式化为对抗性的两人博弈，我们提出了一种新颖的乘法权重优化算法，并在理论上证明了其能够收敛到公平的解。实验结果表明，我们所提出的分类器在五个数据集上能够更公平地分布于各类别，并对平均准确率的影响有限。

基于双方博弈方法提升分类中的类公平性

Enhancing Class Fairness in Classification with A Two-Player Game  Approach

Due to escalating privacy concerns, federated learning has been recognized as
a vital approach for training deep neural networks with decentralized medical
data. In practice, it is challenging to ensure consistent imaging quality
across various institutions, often attributed to equipment malfunctions
affecting a minority of clients. This imbalance in image quality can cause the
federated model to develop an inherent bias towards higher-quality images, thus
posing a severe fairness issue. In this study, we pioneer the identification
and formulation of this new fairness challenge within the context of the
imaging quality shift. Traditional methods for promoting fairness in federated
learning predominantly focus on balancing empirical risks across diverse client
distributions. This strategy primarily facilitates fair optimization across
different training data distributions, yet neglects the crucial aspect of
generalization. To address this, we introduce a solution termed Federated
learning with Inter-client Sharpness Matching (FedISM). FedISM enhances both
local training and global aggregation by incorporating sharpness-awareness,
aiming to harmonize the sharpness levels across clients for fair
generalization. Our empirical evaluations, conducted using the widely-used ICH
and ISIC 2019 datasets, establish FedISM's superiority over current
state-of-the-art federated learning methods in promoting fairness. Code is
available at this https URL

联邦学习中的图像质量不平衡会导致固有偏见，本研究提出了一种名为 FedISM 的解决方案，通过锐度匹配来增强联邦学习的公平性。

从优化到泛化：通过客户端锐度匹配实现公平联邦学习以抵抗质量变化

From Optimization to Generalization: Fair Federated Learning against  Quality Shift via Inter-Client Sharpness Matching

Collaborative filtering based recommendation learns users' preferences from
all users' historical behavior data, and has been popular to facilitate
decision making. R Recently, the fairness issue of recommendation has become
more and more essential. A recommender system is considered unfair when it does
not perform equally well for different user groups according to users'
sensitive attributes~(e.g., gender, race). Plenty of methods have been proposed
to alleviate unfairness by optimizing a predefined fairness goal or changing
the distribution of unbalanced training data. However, they either suffered
from the specific fairness optimization metrics or relied on redesigning the
current recommendation architecture. In this paper, we study how to improve
recommendation fairness from the data augmentation perspective. The
recommendation model amplifies the inherent unfairness of imbalanced training
data. We augment imbalanced training data towards balanced data distribution to
improve fairness. The proposed framework is generally applicable to any
embedding-based recommendation, and does not need to pre-define a fairness
metric. Extensive experiments on two real-world datasets clearly demonstrate
the superiority of our proposed framework. We publish the source code at
this https URL

本文研究了如何从数据增强的角度提高协同过滤推荐的公平性，通过增强数据集中的不平衡数据来改善其公平性，测试结果表明这种方法能有效提高推荐公平性。