  With the arising concerns of privacy within machine learning, federated
learning (FL) was invented in 2017, in which the clients, such as mobile
devices, train a model and send the update to the centralized server. Choosing
clients randomly for FL can harm learning performance due to different reasons.
Many studies have proposed approaches to address the challenges of client
selection of FL. However, no systematic literature review (SLR) on this topic
existed. This SLR investigates the state of the art of client selection in FL
and answers the challenges, solutions, and metrics to evaluate the solutions.
We systematically reviewed 47 primary studies. The main challenges found in
client selection are heterogeneity, resource allocation, communication costs,
and fairness. The client selection schemes aim to improve the original random
selection algorithm by focusing on one or several of the aforementioned
challenges. The most common metric used is testing accuracy versus
communication rounds, as testing accuracy measures the successfulness of the
learning and preferably in as few communication rounds as possible, as they are
very expensive. Although several possible improvements can be made with the
current state of client selection, the most beneficial ones are evaluating the
impact of unsuccessful clients and gaining a more theoretical understanding of
the impact of fairness in FL.
