  Federated learning involves training statistical models over remote devices
such as mobile phones while keeping data localized. Training in heterogeneous
and potentially massive networks introduces opportunities for
privacy-preserving data analysis and diversifying these models to become more
inclusive of the population. Federated learning can be viewed as a unique
opportunity to bring fairness and parity to many existing models by enabling
model training to happen on a diverse set of participants and on data that is
generated regularly and dynamically. In this paper, we discuss the current
metrics and approaches that are available to measure and evaluate fairness in
the context of spatial-temporal models. We propose how these metrics and
approaches can be re-defined to address the challenges that are faced in the
federated learning setting.
