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Federated Learning (FL) is an efficient distributed machine learning paradigmthat employs private datasets in a privacy-preserving manner. The mainchallenges of FL is that end devices usually possess various computation andcommunication capabilities and their training data are not independent andidentically distributed (non-IID). Due to limited communication bandwidth andunstable availability of such devices in a mobile network, only a fraction ofend devices (also referred to as the participants or clients in a FL process)can be selected in each round. Hence, it is of paramount importance to utilizean efficient participant selection scheme to maximize the performance of FLincluding final model accuracy and training time. In this paper, we provide areview of participant selection techniques for FL. First, we introduce FL andhighlight the main challenges during participant selection. Then, we review theexisting studies and categorize them based on their solutions. Finally, weprovide some future directions on participant selection for FL based on ouranalysis of the state-of-the-art in this topic area.

A Survey on Participant Selection for Federated Learning in Mobile Networks