  Deep learning's unprecedented success raises several ethical concerns ranging
from biased predictions to data privacy. Researchers tackle these issues by
introducing fairness metrics, or federated learning, or differential privacy. A
first, this work presents an ethical federated learning model, incorporating
all three measures simultaneously. Experiments on the Adult, Bank and Dutch
datasets highlight the resulting ``empirical interplay" between accuracy,
fairness, and privacy.
