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Federated learning provides the ability to learn over heterogeneous user datain a distributed manner while preserving user privacy. However, its currentclient selection technique is a source of bias as it discriminates against slowclients. For starters, it selects clients that satisfy certain network andsystem-specific criteria, thus not selecting slow clients. Even when suchclients are included in the training process, they either struggle with thetraining or are dropped altogether for being too slow. Our proposed idea looksto find a sweet spot between fast convergence and heterogeneity by looking atsmart client selection and scheduling techniques.

FedSS: Federated Learning with Smart Selection of clients