  While fairness-aware machine learning algorithms have been receiving
increasing attention, the focus has been on centralized machine learning,
leaving decentralized methods underexplored. Federated Learning is a
decentralized form of machine learning where clients train local models with a
server aggregating them to obtain a shared global model. Data heterogeneity
amongst clients is a common characteristic of Federated Learning, which may
induce or exacerbate discrimination of unprivileged groups defined by sensitive
attributes such as race or gender. In this work we propose FAIR-FATE: a novel
FAIR FederATEd Learning algorithm that aims to achieve group fairness while
maintaining high utility via a fairness-aware aggregation method that computes
the global model by taking into account the fairness of the clients. To achieve
that, the global model update is computed by estimating a fair model update
using a Momentum term that helps to overcome the oscillations of non-fair
gradients. To the best of our knowledge, this is the first approach in machine
learning that aims to achieve fairness using a fair Momentum estimate.
Experimental results on real-world datasets demonstrate that FAIR-FATE
outperforms state-of-the-art fair Federated Learning algorithms under different
levels of data heterogeneity.
