As methods to create discrimination-aware models develop, they focus on
centralized ML, leaving federated learning (FL) unexplored. FL is a rising
approach for collaborative ML, in which an aggregator orchestrates multiple
parties to train a global model without sharing their training data. In this
paper, we discuss causes of bias in FL and propose three pre-processing and
in-processing methods to mitigate bias, without compromising data privacy, a
key FL requirement. As data heterogeneity among parties is one of the
challenging characteristics of FL, we conduct experiments over several data
distributions to analyze their effects on model performance, fairness metrics,
and bias learning patterns. We conduct a comprehensive analysis of our proposed
techniques, the results demonstrating that these methods are effective even
when parties have skewed data distributions or as little as 20% of parties
employ the methods.

本研究提出了三种预处理和处理方法以及实验验证，以减轻联邦学习中的偏见和数据异构性对模型性能、公平指标和偏见学习模式的影响，结果表明这些方法即使在当事方数据分布不均或只有 20% 的当事方采用这些方法时也是有效的。