Interview with Teresa Salazar: Developing fair federated learning algorithms

In their paper FAIR-FATE: Fair Federated Learning with Momentum, Teresa Salazar, Miguel Fernandes, Helder Araujo, and Pedro Henriques Abreu develop a fairness-aware federated learning algorithm which aims to achieve group fairness while maintaining classification performance. Here, Teresa tells us more about their work. With the widespread use of machine learning algorithms to make decisions which impact people's lives, the area of fairness-aware machine learning has been receiving increasing attention. Fairness-aware machine learning algorithms ensure that predictions do not prejudice unprivileged groups of the population with respect to sensitive attributes such as race or gender. However, the focus has been on centralized machine learning, with decentralized methods receiving little attention.