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.

翻译：在联邦学习领域，处理非独立同分布（non-i.i.d.）数据仍是提升全局模型性能的关键挑战。本文提出特征范数正则化联邦学习（FNR-FL）算法，该算法创新性地引入类别平均特征范数，以增强模型在非独立同分布场景下的准确性与收敛性。全面分析表明，FNR-FL不仅加速了收敛过程，而且在测试准确率上显著超越其他当代联邦学习算法，尤其在特征分布偏斜场景下表现突出。FNR-FL新颖的模块化设计使其能够无缝集成至现有联邦学习框架中，强化了其适应性与广泛应用潜力。我们通过严格的实证评估验证了上述论断，展示了FNR-FL在多种偏斜数据分布下的卓越性能。相较于FedAvg，FNR-FL在准确率上实现了66.24%的显著提升，同时训练时间减少了11.40%，充分凸显了其增强的有效性与效率。