Federated Contrastive Graph Attention Neural Network With Correlation Embedding for Noisy Multi-Label Classification

Multi-label image classification in privacy-sensitive domains faces several challenges, particularly in multi-label electricity scene classification tasks where equipment wear and similar factors introduce label noise. The fundamental limitation lies in most FL algorithms’ implicit assumption of clean local datasets, making them inherently susceptible to performance degradation when faced with the realistic challenges of noisy labels in distributed learning scenarios. Moreover, in current federated learning methods for noisy multi-label classification, few approaches consider modeling the correlations between labels to assist in the classification task of noisy labels. Specifically, they rarely address the differences in correlations between noisy labels and true labels, as well as between true labels themselves. Additionally, when noisy labels are present on the client side, the model is prone to overfitting to these noisy labels, which negatively impacts its recognition capability. Under this situation, this paper proposes a federated contrastive graph attention neural network with correlation embedding (FedCGAT) for noisy multi-label classification. FedCGAT alleviates the impact of noisy labels by introducing consistency constraints on client models to build consistent feature representations. Specifically, FedCGAT designs client-side graph structures to capture label semantic information, leveraging label correlations to provide corrective guidance for noisy annotations. Furthermore, it designs a dual-branch contrastive learning framework integrating local and global models to construct model support for label denoising. This dual-pronged approach jointly learns discriminative features from both label semantics and model representations, thereby achieving robust noisy multi-label classification. Experimental validation on federated electricity scene image datasets confirms the effectiveness of the proposed model.