  Federated Learning (FL) is a collaborative method for training models while
preserving data privacy in decentralized settings. However, FL encounters
challenges related to data heterogeneity, which can result in performance
degradation. In our study, we observe that as data heterogeneity increases,
feature representation in the FedAVG model deteriorates more significantly
compared to classifier weight. Additionally, we observe that as data
heterogeneity increases, the gap between higher feature norms for observed
classes, obtained from local models, and feature norms of unobserved classes
widens, in contrast to the behavior of classifier weight norms. This widening
gap extends to encompass the feature norm disparities between local and the
global models. To address these issues, we introduce Federated Averaging with
Feature Normalization Update (FedFN), a straightforward learning method. We
demonstrate the superior performance of FedFN through extensive experiments,
even when applied to pretrained ResNet18. Subsequently, we confirm the
applicability of FedFN to foundation models.
