  Federated Learning (FL) enables statistical models to be built on
user-generated data without compromising data security and user privacy. For
this reason, FL is well suited for on-device learning from mobile devices where
data is abundant and highly privatized. Constrained by the temporal
availability of mobile devices, only a subset of devices is accessible to
participate in the iterative protocol consisting of training and aggregation.
In this study, we take a step toward better understanding the effect of
non-independent data distributions arising from block-cyclic sampling. By
conducting extensive experiments on visual classification, we measure the
effects of block-cyclic sampling (both standalone and in combination with
non-balanced block distributions). Specifically, we measure the alterations
induced by block-cyclic sampling from the perspective of accuracy, fairness,
and convergence rate. Experimental results indicate robustness to cycling over
a two-block structure, e.g., due to time zones. In contrast, drawing data
samples dependently from a multi-block structure significantly degrades the
performance and rate of convergence by up to 26%. Moreover, we find that this
performance degeneration is further aggravated by unbalanced block
distributions to a point that can no longer be adequately compensated by higher
communication and more frequent synchronization.
