{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:25:06Z","timestamp":1760059506521,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science Research Project of Weinan Science and Technology Bureau","award":["ZWGX2003-1"],"award-info":[{"award-number":["ZWGX2003-1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/sym17060969","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:38:15Z","timestamp":1750232295000},"page":"969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Homomorphic Encryption-Based Federated Active Learning on GCNs"],"prefix":"10.3390","volume":"17","author":[{"given":"Xiaohu","family":"He","sequence":"first","affiliation":[{"name":"School of Computer Science, Weinan Normal University, Weinan 714099, China"}]},{"given":"Zhihao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University, Rizhao 276800, China"}]},{"given":"Dandan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University, Rizhao 276800, China"}]},{"given":"Hongwei","family":"Ju","sequence":"additional","affiliation":[{"name":"Experimental Teaching and Equipment Management Center, Qufu Normal University, Rizhao 276800, China"}]},{"given":"Qingfang","family":"Meng","sequence":"additional","affiliation":[{"name":"Affiliated Experimental School, Qufu Normal University, Rizhao 276800, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Caramalau, R., Bhattarai, B., and Kim, T.K. (2021, January 20\u201325). Sequential graph convolutional network for active learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00946"},{"key":"ref_2","unstructured":"Tong, Z., Liang, Y., and Sun, C. (2020). Directed graph convolutional network. arXiv."},{"key":"ref_3","unstructured":"Wu, Y., Xu, Y., and Singh, A. (2019). Active learning for graph neural networks via node feature propagation. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, X., Wu, Y., and Rakesh, V. (2022, January 17\u201321). Smartquery: An active learning framework for graph neural networks through hybrid uncertainty reduction. Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Atlanta, GA, USA.","DOI":"10.1145\/3511808.3557701"},{"key":"ref_5","first-page":"10174","article-title":"Graph policy network for transferable active learning on graphs","volume":"Volume 33","author":"Hu","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_6","first-page":"9118","article-title":"Batch active learning with graph neural networks via multi-agent deep reinforcement learning","volume":"36","author":"Zhang","year":"2022","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, W., Shen, Y., and Li, Y. (2021, January 20\u201325). Alg: Fast and accurate active learning framework for graph convolutional networks. Proceedings of the 2021 International Conference on Management of Data, Xi\u2019an, China.","DOI":"10.1145\/3448016.3457325"},{"key":"ref_8","unstructured":"Cai, H., Zheng, V.W., and Chang, K.C.C. (2017). Active learning for graph embedding. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Gao, L., Yang, H., and Zhou, C. (2018, January 13\u201319). Active discriminative network representation learning. Proceedings of the IJCAI International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/296"},{"key":"ref_10","unstructured":"Sener, O., and Savarese, S. (2017). Active learning for convolutional neural networks: A core-set approach. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Joshi, A.J., Porikli, F., and Papanikolopoulos, N. (2009, January 20\u201325). Multi-class active learning for image classification. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206627"},{"key":"ref_12","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph Neural Networks: A Review of Methods and Applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chen, C., Weiss, S.T., and Liu, Y.Y. (2023). Graph convolutional network-based feature selection for high-dimensional and low-sample size data. Bioinformatics, 39.","DOI":"10.1093\/bioinformatics\/btad135"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"108661","DOI":"10.1016\/j.patcog.2022.108661","article-title":"Node-feature convolution for graph convolutional networks","volume":"128","author":"Zhang","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s40649-019-0069-y","article-title":"Graph convolutional networks: A comprehensive review","volume":"6","author":"Zhang","year":"2019","journal-title":"Comput. Soc. Netw."},{"key":"ref_17","unstructured":"Wu, F., Souza, A., and Zhang, T. (2019, January 9\u201315). Simplifying graph convolutional networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_18","unstructured":"Chen, M., Wei, Z., and Huang, Z. (2020, January 13\u201318). Simple and deep graph convolutional networks. Proceedings of the International Conference on Machine Learning, PMLR, Vienna, Austria."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gao, H., Wang, Z., and Ji, S. (2018, January 19\u201323). Large-scale learnable graph convolutional networks. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK.","DOI":"10.1145\/3219819.3219947"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107000","DOI":"10.1016\/j.patcog.2019.107000","article-title":"Dynamic graph convolutional networks","volume":"97","author":"Manessi","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_21","first-page":"7370","article-title":"Graph convolutional networks for text classification","volume":"33","author":"Yao","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M. (2018, January 3\u20137). Modeling relational data with graph convolutional networks. Proceedings of the 15th International Conference, ESWC 2018, Heraklion, Greece.","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ma, Y., Wang, S., and Aggarwal, C.C. (2019, January 4\u20138). Graph convolutional networks with eigenpooling. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330982"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yu, P., Fu, C., and Yu, Y. (2022, January 14\u201318). Multiplex heterogeneous graph convolutional network. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/3534678.3539482"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3599","DOI":"10.1109\/TCYB.2022.3159661","article-title":"A novel representation learning for dynamic graphs based on graph convolutional networks","volume":"53","author":"Gao","year":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TNNLS.2022.3172588","article-title":"Multigraph fusion for dynamic graph convolutional network","volume":"35","author":"Gan","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TBDATA.2021.3140205","article-title":"Active and semi-supervised graph neural networks for graph classification","volume":"8","author":"Xie","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.inffus.2023.02.013","article-title":"Learnable graph convolutional network and feature fusion for multi-view learning","volume":"95","author":"Chen","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kurniawan, H., and Mambo, M. (2022). Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning. Entropy, 24.","DOI":"10.3390\/e24111545"},{"key":"ref_30","unstructured":"Zhang, C.L., Li, S., and Xia, J.Z. (2020, January 15\u201317). Batchcrypt: Efficient homomorphic encryption for cross-silo federated learning. Proceedings of the 2020 USENIX Annual Technical Conference (USENIX ATC 2020), Boston, MA, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Angulo, E., Marquez, J., and Villanueva-Polanco, R. (2023). Training of Classification Models via Federated Learning and Homomorphic Encryption. Sensors, 23.","DOI":"10.3390\/s23041966"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Awan, S., Luo, B., and Li, F.J. (2021, January 4\u20138). Contra: Defending against poisoning attacks in federated learning. Proceedings of the Computer Security\u2014ESORICS 2021: 26th European Symposium on Research in Computer Security, Darmstadt, Germany. Part I 26.","DOI":"10.1007\/978-3-030-88418-5_22"},{"key":"ref_33","unstructured":"Gao, X.Y., and Gong, N.Z.Q. (2022, January 21\u201324). Mpaf: Model poisoning attacks to federated learning based on fake clients. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, X.C., Xu, M., Wu, Y.M., and Zheng, N. (2021). Deep model poisoning attack on federated learning. Future Internet, 13.","DOI":"10.3390\/fi13030073"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103295","DOI":"10.1016\/j.rineng.2024.103295","article-title":"A comprehensive analysis of model poisoning attacks in federated learning for autonomous vehicles: A benchmark study","volume":"24","author":"Almutairi","year":"2024","journal-title":"Results Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101765","DOI":"10.1016\/j.softx.2024.101765","article-title":"Federated learning secure model: A framework for malicious clients detection","volume":"27","author":"Kolasa","year":"2024","journal-title":"SoftwareX"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"103406","DOI":"10.1016\/j.cose.2023.103406","article-title":"MUD-PQFed: Towards Malicious User Detection on model corruption in Privacy-preserving Quantized Federated learning","volume":"133","author":"Ma","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10708","DOI":"10.1109\/ACCESS.2023.3238823","article-title":"Poisoning Attacks in Federated Learning: A Survey","volume":"11","author":"Xia","year":"2023","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1007\/s40747-024-01664-0","article-title":"A survey of security threats in federated learning","volume":"11","author":"Feng","year":"2025","journal-title":"Complex Intell. Syst."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/969\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:54:04Z","timestamp":1760032444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/6\/969"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["sym17060969"],"URL":"https:\/\/doi.org\/10.3390\/sym17060969","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2025,6,18]]}}}