  Federated learning (FL) has been proposed as a privacy-preserving approach in
distributed machine learning. A federated learning architecture consists of a
central server and a number of clients that have access to private, potentially
sensitive data. Clients are able to keep their data in their local machines and
only share their locally trained model's parameters with a central server that
manages the collaborative learning process. FL has delivered promising results
in real-life scenarios, such as healthcare, energy, and finance. However, when
the number of participating clients is large, the overhead of managing the
clients slows down the learning. Thus, client selection has been introduced as
a strategy to limit the number of communicating parties at every step of the
process. Since the early na\"{i}ve random selection of clients, several client
selection methods have been proposed in the literature. Unfortunately, given
that this is an emergent field, there is a lack of a taxonomy of client
selection methods, making it hard to compare approaches. In this paper, we
propose a taxonomy of client selection in Federated Learning that enables us to
shed light on current progress in the field and identify potential areas of
future research in this promising area of machine learning.

Distributed, Parallel, and Cluster Computing;Artificial Intelligence;Cryptography and Security;Machine Learning