  Federated Learning (FL) enables distributed training by learners using local
data, thereby enhancing privacy and reducing communication. However, it
presents numerous challenges relating to the heterogeneity of the data
distribution, device capabilities, and participant availability as deployments
scale, which can impact both model convergence and bias. Existing FL schemes
use random participant selection to improve fairness; however, this can result
in inefficient use of resources and lower quality training. In this work, we
systematically address the question of resource efficiency in FL, showing the
benefits of intelligent participant selection, and incorporation of updates
from straggling participants. We demonstrate how these factors enable resource
efficiency while also improving trained model quality.
