  Layer normalization (LN) is a widely adopted deep learning technique
especially in the era of foundation models. Recently, LN has been shown to be
surprisingly effective in federated learning (FL) with non-i.i.d. data.
However, exactly why and how it works remains mysterious. In this work, we
reveal the profound connection between layer normalization and the label shift
problem in federated learning. To understand layer normalization better in FL,
we identify the key contributing mechanism of normalization methods in FL,
called feature normalization (FN), which applies normalization to the latent
feature representation before the classifier head. Although LN and FN do not
improve expressive power, they control feature collapse and local overfitting
to heavily skewed datasets, and thus accelerates global training. Empirically,
we show that normalization leads to drastic improvements on standard benchmarks
under extreme label shift. Moreover, we conduct extensive ablation studies to
understand the critical factors of layer normalization in FL. Our results
verify that FN is an essential ingredient inside LN to significantly improve
the convergence of FL while remaining robust to learning rate choices,
especially under extreme label shift where each client has access to few
classes. Our code is available at
\url{https://github.com/huawei-noah/Federated-Learning/tree/main/Layer_Normalization}.
