  Training and deploying Machine Learning models that simultaneously adhere to
principles of fairness and privacy while ensuring good utility poses a
significant challenge. The interplay between these three factors of
trustworthiness is frequently underestimated and remains insufficiently
explored. Consequently, many efforts focus on ensuring only two of these
factors, neglecting one in the process. The decentralization of the datasets
and the variations in distributions among the clients exacerbate the complexity
of achieving this ethical trade-off in the context of Federated Learning (FL).
For the first time in FL literature, we address these three factors of
trustworthiness. We introduce PUFFLE, a high-level parameterised approach that
can help in the exploration of the balance between utility, privacy, and
fairness in FL scenarios. We prove that PUFFLE can be effective across diverse
datasets, models, and data distributions, reducing the model unfairness up to
75%, with a maximum reduction in the utility of 17% in the worst-case scenario,
while maintaining strict privacy guarantees during the FL training.
