FLex&Chill: Improving Local Federated Learning Training with Logit
  Chilling
Kichang Lee Songkuk Kim JeongGil Ko
Federated learning are inherently hampered by data heterogeneity: non-iid
distributed 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-iid 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.