Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning
This addresses a specific, inherent data issue in federated learning, offering an incremental improvement for model fairness and performance.
The paper tackles the problem of inherent inter-class accuracy discrepancies in federated learning, even with balanced data, and proposes a partial knowledge distillation method that improves weak class accuracy by 10.7%.
Substantial efforts have been devoted to alleviating the impact of the long-tailed class distribution in federated learning. In this work, we observe an interesting phenomenon that certain weak classes consistently exist even for class-balanced learning. These weak classes, different from the minority classes in the previous works, are inherent to data and remain fairly consistent for various network structures, learning paradigms, and data partitioning methods. The inherent inter-class accuracy discrepancy can reach over 36.9% for federated learning on the FashionMNIST and CIFAR-10 datasets, even when the class distribution is balanced both globally and locally. In this study, we empirically analyze the potential reason for this phenomenon. Furthermore, a partial knowledge distillation (PKD) method is proposed to improve the model's classification accuracy for weak classes. In this approach, knowledge transfer is initiated upon the occurrence of specific misclassifications within certain weak classes. Experimental results show that the accuracy of weak classes can be improved by 10.7%, reducing the inherent inter-class discrepancy effectively.