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