Federated learning (FL) systems are susceptible to attacks from malicious
actors who might attempt to corrupt the training model through various
poisoning attacks. FL also poses new challenges in addressing group bias, such
as ensuring fair performance for different demographic groups. Traditional
methods used to address such biases require centralized access to the data,
which FL systems do not have. In this paper, we present a novel approach FedVal
for both robustness and fairness that does not require any additional
information from clients that could raise privacy concerns and consequently
compromise the integrity of the FL system. To this end, we propose an
innovative score function based on a server-side validation method that
assesses client updates and determines the optimal aggregation balance between
locally-trained models. Our research shows that this approach not only provides
solid protection against poisoning attacks but can also be used to reduce group
bias and subsequently promote fairness while maintaining the system's
capability for differential privacy. Extensive experiments on the CIFAR-10,
FEMNIST, and PUMS ACSIncome datasets in different configurations demonstrate
the effectiveness of our method, resulting in state-of-the-art performances. We
have proven robustness in situations where 80% of participating clients are
malicious. Additionally, we have shown a significant increase in accuracy for
underrepresented labels from 32% to 53%, and increase in recall rate for
underrepresented features from 19% to 50%.

本论文提出了一种名为 FedVal 的新方法，该方法基于服务器端验证方法，使用创新的评分函数来评估客户端更新，并确定本地训练模型之间的最佳聚合平衡，从而提高鲁棒性和公平性，同时维护差分隐私的系统能力。在 CIFAR-10，FEMNIST 和 PUMS ACSIncome 数据集上的广泛实验证明了我们的方法的有效性，实现了最先进的性能。

联邦学习中的异质性好还是坏：FedVal

FedVal: Different good or different bad in federated learning

Group bias in natural language processing tasks manifests as disparities in
system error rates across texts authorized by different demographic groups,
typically disadvantaging minority groups. Dataset balancing has been shown to
be effective at mitigating bias, however existing approaches do not directly
account for correlations between author demographics and linguistic variables,
limiting their effectiveness. To achieve Equal Opportunity fairness, such as
equal job opportunity without regard to demographics, this paper introduces a
simple, but highly effective, objective for countering bias using balanced
training. We extend the method in the form of a gated model, which incorporates
protected attributes as input, and show that it is effective at reducing bias
in predictions through demographic input perturbation, outperforming all other
bias mitigation techniques when combined with balanced training.

该研究旨在解决自然语言处理中与作者人口统计信息相关的系统偏差问题，提出了一种基于数据集平衡和带保护属性的门控模型来实现公正性，证明了该方法在减少偏差方面优于其他技术。