FML-DGCN: Federated Multi-Label Learning Based on Dynamic Graph Convolutional Networks

Shaocong Xue, Wenjian Luo, Zeping Yin, Jiahao Gu, Yamin Hu

Abstract: Federated multilabel learning enables the collaborative training of multilabel classification models while preserving client privacy. Existing federated multilabel learning methods either fail to effectively capture label correlations, which significantly affects model performance in scenarios where labels are interdependent, or increase the risk of client privacy leakage due to the transmission of unnecessary client data. To this end, we propose FML-DGCN, a federated multi-label learning approach based on dynamic graph convolutional networks. In the local training phase, a dynamic graph convolutional module is designed and employed to generate label representations specific to the input image, with parameters efficiently optimized by our designed correlation alignment loss. Within the module, we employ a fully connected layer-based network to merge static label embeddings and image features for computing dynamic label graphs, instead of leveraging complex attention-based networks, making it suitable for federated learning environments with limited computing resources. In the federated aggregation phase, clients transmit only model parameters, without sharing any additional information such as scene knowledge, thus lowering the risk of client privacy leakage. Experimental results on four typical multilabel image classification datasets demonstrate the superiority of our approach.

External IDs:doi:10.1109/tcss.2026.3675328