Feature Norm Regularized Federated Learning: Transforming Skewed
  Distributions into Global Insights
Ke Hu WeiDong Qiu Peng Tang
In the field of federated learning, addressing non-independent and
identically distributed (non-i.i.d.) data remains a quintessential challenge
for improving global model performance. This work introduces the Feature Norm
Regularized Federated Learning (FNR-FL) algorithm, which uniquely incorporates
class average feature norms to enhance model accuracy and convergence in
non-i.i.d. scenarios. Our comprehensive analysis reveals that FNR-FL not only
accelerates convergence but also significantly surpasses other contemporary
federated learning algorithms in test accuracy, particularly under feature
distribution skew scenarios. The novel modular design of FNR-FL facilitates
seamless integration with existing federated learning frameworks, reinforcing
its adaptability and potential for widespread application. We substantiate our
claims through rigorous empirical evaluations, demonstrating FNR-FL's
exceptional performance across various skewed data distributions. Relative to
FedAvg, FNR-FL exhibits a substantial 66.24\% improvement in accuracy and a
significant 11.40\% reduction in training time, underscoring its enhanced
effectiveness and efficiency.