  Federated learning is inherently hampered by data heterogeneity: non-i.i.d.
training data over local clients. We propose a novel model training approach
for federated learning, FLex&Chill, which exploits the Logit Chilling method.
Through extensive evaluations, we demonstrate that, in the presence of
non-i.i.d. data characteristics inherent in federated learning systems, this
approach can expedite model convergence and improve inference accuracy.
Quantitatively, from our experiments, we observe up to 6X improvement in the
global federated learning model convergence time, and up to 3.37% improvement
in inference accuracy.
