  Federated learning enables machine learning models to learn from private
decentralized data without compromising privacy. The standard formulation of
federated learning produces one shared model for all clients. Statistical
heterogeneity due to non-IID distribution of data across devices often leads to
scenarios where, for some clients, the local models trained solely on their
private data perform better than the global shared model thus taking away their
incentive to participate in the process. Several techniques have been proposed
to personalize global models to work better for individual clients. This paper
highlights the need for personalization and surveys recent research on this
topic.
