Homomorphic Encryption-Based Federated Active Learning on GCNs
Xiaohu He, Zhihao Song, Dandan Zhang, Hongwei Ju, Qingfang Meng · Symmetry · 2025
With the dramatic growth in dataset size, active learning has become one of the effective methods to deal with large-scale unlabeled data. However, most of the existing active learning methods are inefficient due poor target models and lack the ability to utilize the feature similarity between labeled and unlabeled data. Furthermore, data leakage is a serious threat to data privacy. In this paper, considering the features of the data itself, an augmented graph convolutional network is proposed which acts as a sampler for data selection in active learning, avoiding the involvement of the initial poor target model. Then, by applying the proposed GCN as a substitute for the initial poor target model, this paper proposes an active learning model based on augmented GCNs, which is able to select more representative data, enabling the active learning model to achieve better classification performance with limited labeled data. Finally, this paper proposes a homomorphic encryption-based federated active learning model to improve the data utilization and enhance the security of private data. Experiments were conducted on three datasets, Cora, CiteSeer and PubMed, and achieved accuracy rates of 94.47%, 92.86% and 91.51%, respectively, while providing provable security guarantees. Furthermore, the highest malicious user detection accuracy was 88.07%, and the global model test accuracy reached 88.42%, 84.22% and 81.46%, under a model poisoning attack.