  Federated learning (FL) enhances data privacy with collaborative in-situ
training on decentralized clients. Nevertheless, FL encounters challenges due
to non-independent and identically distributed (non-i.i.d) data, leading to
potential performance degradation and hindered convergence. While prior studies
predominantly addressed the issue of skewed label distribution, our research
addresses a crucial yet frequently overlooked problem known as multi-domain FL.
In this scenario, clients' data originate from diverse domains with distinct
feature distributions, instead of label distributions. To address the
multi-domain problem in FL, we propose a novel method called Federated learning
Without normalizations (FedWon). FedWon draws inspiration from the observation
that batch normalization (BN) faces challenges in effectively modeling the
statistics of multiple domains, while existing normalization techniques possess
their own limitations. In order to address these issues, FedWon eliminates the
normalization layers in FL and reparameterizes convolution layers with scaled
weight standardization. Through extensive experimentation on five datasets and
five models, our comprehensive experimental results demonstrate that FedWon
surpasses both FedAvg and the current state-of-the-art method (FedBN) across
all experimental setups, achieving notable accuracy improvements of more than
10% in certain domains. Furthermore, FedWon is versatile for both cross-silo
and cross-device FL, exhibiting robust domain generalization capability,
showcasing strong performance even with a batch size as small as 1, thereby
catering to resource-constrained devices. Additionally, FedWon can also
effectively tackle the challenge of skewed label distribution.
