Robustness and Privacy for Green Learning under Noisy Labels

As a new paradigm of machine learning, green learning has achieved performance comparable to deep learning in vision tasks, knowledge graph learning, and modeling graph structures. Compared with deep learning, green learning has the advantages of a low carbon footprint, lightweight model and logical transparency. In this paper, to enhance the robustness of green learning, we study the performance of the green model when the training dataset labels are noisy. Through theoretical analysis and experimental verification, we found that noisy labels will not only reduce the performance of green learning, but also amplify the risk of privacy leaks of training dataset members. Therefore, we propose a robust green learning to approach the above two threats. Specifically, we design a multi-stage label-consistency sample selection method to filter out noisy labels in the training dataset by taking advantage of the characteristics of unsupervised representation learning in green learning. In order to further improve the stable classification performance of the model, we propose a feature-level class-balanced data augmentation method in the feature decision learning stage to solve the class imbalance problem caused by sample selection. Finally, a large number of experiments on multiple noisy datasets show that the robust green learning method proposed in this paper can not only enhance the generalization of the model under noisy training labels, but also alleviate the risk of member privacy information leakage.