Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global Insights

• URL: http://arxiv.org/abs/2312.06951v1
• Date: Tue, 12 Dec 2023 03:09:37 GMT
• Title: Feature Norm Regularized Federated Learning: Transforming Skewed Distributions into Global Insights
• Authors: Ke Hu, WeiDong Qiu, Peng Tang
• Abstract summary: This work introduces the Feature Norm Regularized Federated Learning (FNR-FL) algorithm. FNR-FL incorporates class average feature norms to enhance model accuracy and convergence in non-i.i.d. scenarios. We show that FNR-FL exhibits a substantial 66.24% improvement in accuracy and a significant 11.40% reduction in training time.
• Score: 16.039822050613022
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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