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Transformer-based architectures have been the subject of research aimed atunderstanding their overparameterization and the non-uniform importance oftheir layers. Applying these approaches to Automatic Speech Recognition, wedemonstrate that the state-of-the-art Conformer models generally have multipleambient layers. We study the stability of these layers across runs and modelsizes, propose that group normalization may be used without disrupting theirformation, and examine their correlation with model weight updates in eachlayer. Finally, we apply these findings to Federated Learning in order toimprove the training procedure, by targeting Federated Dropout to layers byimportance. This allows us to reduce the model size optimized by clientswithout quality degradation, and shows potential for future exploration.

Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training