  Training ML models which are fair across different demographic groups is of
critical importance due to the increased integration of ML in crucial
decision-making scenarios such as healthcare and recruitment. Federated
learning has been viewed as a promising solution for collaboratively training
machine learning models among multiple parties while maintaining the privacy of
their local data. However, federated learning also poses new challenges in
mitigating the potential bias against certain populations (e.g., demographic
groups), as this typically requires centralized access to the sensitive
information (e.g., race, gender) of each datapoint. Motivated by the importance
and challenges of group fairness in federated learning, in this work, we
propose FairFed, a novel algorithm for fairness-aware aggregation to enhance
group fairness in federated learning. Our proposed approach is server-side and
agnostic to the applied local debiasing thus allowing for flexible use of
different local debiasing methods across clients. We evaluate FairFed
empirically versus common baselines for fair ML and federated learning, and
demonstrate that it provides fairer models particularly under highly
heterogeneous data distributions across clients. We also demonstrate the
benefits of FairFed in scenarios involving naturally distributed real-life data
collected from different geographical locations or departments within an
organization.
