A fair classifier should ensure the benefit of people from different groups,
while the group information is often sensitive and unsuitable for model
training. Therefore, learning a fair classifier but excluding sensitive
attributes in the training dataset is important. In this paper, we study
learning fair classifiers without implementing fair training algorithms to
avoid possible leakage of sensitive information. Our theoretical analyses
validate the possibility of this approach, that traditional training on a
dataset with an appropriate distribution shift can reduce both the upper bound
for fairness disparity and model generalization error, indicating that fairness
and accuracy can be improved simultaneously with simply traditional training.
We then propose a tractable solution to progressively shift the original
training data during training by sampling influential data, where the sensitive
attribute of new data is not accessed in sampling or used in training.
Extensive experiments on real-world data demonstrate the effectiveness of our
proposed algorithm.

学习公平分类器的方法是避免使用敏感属性的训练数据，在适当分布转移的数据集上进行传统训练可以同时减少公平差距上界和模型泛化误差，提高公平性和准确性。我们提出了一种可行的解决方案，通过采样有影响力的数据来在训练过程中逐步转移原始训练数据，其中新数据的敏感属性不会被访问或用于训练。对真实数据的大量实验验证了我们提出算法的有效性。

无需公平训练的公平分类器：一种受影响引导的数据采样方法

Fair Classifiers Without Fair Training: An Influence-Guided Data  Sampling Approach

In this paper, we study the behaviour of the triplet loss and show that it
can be exploited to limit the biases created and perpetuated by machine
learning models. Our fair classifier uses the collapse of the triplet loss when
its margin is greater than the maximum distance between two points in the
latent space, in the case of stochastic triplet selection.

本文研究三元损失函数的行为，表明在随机三元组选择的情况下，当三元损失函数的边缘大于潜空间中两点之间的最大距离时，可以利用其崩溃现象来限制机器学习模型所创造和长期保持的偏见，从而得到公平的分类器。

一种包含三元组折叠的公平分类器

A Fair Classifier Embracing Triplet Collapse

Recently, lots of algorithms have been proposed for learning a fair
classifier from decentralized data. However, many theoretical and algorithmic
questions remain open. First, is federated learning necessary, i.e., can we
simply train locally fair classifiers and aggregate them? In this work, we
first propose a new theoretical framework, with which we demonstrate that
federated learning can strictly boost model fairness compared with such
non-federated algorithms. We then theoretically and empirically show that the
performance tradeoff of FedAvg-based fair learning algorithms is strictly worse
than that of a fair classifier trained on centralized data. To bridge this gap,
we propose FedFB, a private fair learning algorithm on decentralized data. The
key idea is to modify the FedAvg protocol so that it can effectively mimic the
centralized fair learning. Our experimental results show that FedFB
significantly outperforms existing approaches, sometimes matching the
performance of the centrally trained model.

本文提出了一个新的理论框架，证明联邦学习可以比本地分类器严格提高模型的公平性，但目前的基于 FedAvg 的公平学习算法在性能上与中心化数据上训练的公平分类器相比牺牲了很多，为此我们提出了一种名为 FedFB 的基于隐私保护的公平学习算法，在分散数据上训练时可以有效地模拟中心化的公平学习，实验结果表明，FedFB 明显优于现有方法，有时甚至能够与中心训练模型的性能匹配。