  Federated Learning is a novel framework that allows multiple devices or
institutions to train a machine learning model collaboratively while preserving
their data private. This decentralized approach is prone to suffer the
consequences of data statistical heterogeneity, both across the different
entities and over time, which may lead to a lack of convergence. To avoid such
issues, different methods have been proposed in the past few years. However,
data may be heterogeneous in lots of different ways, and current proposals do
not always determine the kind of heterogeneity they are considering. In this
work, we formally classify data statistical heterogeneity and review the most
remarkable learning strategies that are able to face it. At the same time, we
introduce approaches from other machine learning frameworks, such as Continual
Learning, that also deal with data heterogeneity and could be easily adapted to
the Federated Learning settings.
