  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.
