论文标题
“您无法修复无法衡量的内容”：私人衡量联合学习中的人口统计学差异
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning
论文作者
论文摘要
与传统的机器学习模型一样，接受联合学习的训练的模型可能会在人群之间表现出不同的表现。模型持有人必须确定这些差异，以减轻对这些群体的不必要伤害。但是，测量模型在小组中的绩效需要访问有关团体成员资格的信息，该信息出于隐私原因通常具有有限的可用性。我们提出了新颖的本地私人机制，以衡量跨群体的绩效差异，同时保护小组成员的隐私。为了分析机制的有效性，我们在针对给定的隐私预算进行优化时限制了它们的错误估计差异。我们的结果表明，对于参与的客户数量的实际数量，错误迅速减少，这表明，与先前的工作相反，保护隐私不一定与确定联合模型的绩效差异相抵触。
As in traditional machine learning models, models trained with federated learning may exhibit disparate performance across demographic groups. Model holders must identify these disparities to mitigate undue harm to the groups. However, measuring a model's performance in a group requires access to information about group membership which, for privacy reasons, often has limited availability. We propose novel locally differentially private mechanisms to measure differences in performance across groups while protecting the privacy of group membership. To analyze the effectiveness of the mechanisms, we bound their error in estimating a disparity when optimized for a given privacy budget. Our results show that the error rapidly decreases for realistic numbers of participating clients, demonstrating that, contrary to what prior work suggested, protecting privacy is not necessarily in conflict with identifying performance disparities of federated models.
