  Transformer-based architectures have been the subject of research aimed at
understanding their overparameterization and the non-uniform importance of
their layers. Applying these approaches to Automatic Speech Recognition, we
demonstrate that the state-of-the-art Conformer models generally have multiple
ambient layers. We study the stability of these layers across runs and model
sizes, propose that group normalization may be used without disrupting their
formation, and examine their correlation with model weight updates in each
layer. Finally, we apply these findings to Federated Learning in order to
improve the training procedure, by targeting Federated Dropout to layers by
importance. This allows us to reduce the model size optimized by clients
without quality degradation, and shows potential for future exploration.
