Abstract
Graph Neural Network based Vertical Federated Learning (GVFL) facilitates data collaboration while preserving data privacy by learning GNN-based node representations from participants holding different dimensions of node features. Existing works have shown that GVFL is vulnerable to adversarial attacks from malicious participants. However, how to defend against various adversarial attacks has not been investigated under the non-i.i.d. nature of graph data and privacy constraints. In this paper, we propose RDC-GVFL, a novel two-phase robust GVFL framework. In the detection phase, we adapt a Shapley-based method to evaluate the contribution of all participants to identify malicious ones. In the correction phase, we leverage historical embeddings to rectify malicious embeddings, thereby obtaining accurate predictions. We conducted extensive experiments on three well-known graph datasets under four adversarial attack settings. Our experimental results demonstrate that RDC-GVFL can effectively detect malicious participants and ensure a robust GVFL model against diverse attacks. Our code and supplemental material is available at https://github.com/zcyang-cs/RDC-GVFL.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blanchard, P., El Mhamdi, E.M., Guerraoui, R., Stainer, J.: Machine learning with adversaries: byzantine tolerant gradient descent. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Chen, C., et al.: Vertically federated graph neural network for privacy-preserving node classification. arXiv preprint arXiv:2005.11903 (2020)
Chen, J., Huang, G., Zheng, H., Yu, S., Jiang, W., Cui, C.: Graph-Fraudster: adversarial attacks on graph neural network-based vertical federated learning. IEEE Trans. Comput. Soc. Syst. 10, 492–506 (2022)
Chen, L., Li, J., Peng, Q., Liu, Y., Zheng, Z., Yang, C.: Understanding structural vulnerability in graph convolutional networks. arXiv preprint arXiv:2108.06280 (2021)
Feng, W., et al.: Graph random neural networks for semi-supervised learning on graphs. Adv. Neural. Inf. Process. Syst. 33, 22092–22103 (2020)
Fu, X., Zhang, B., Dong, Y., Chen, C., Li, J.: Federated graph machine learning: a survey of concepts, techniques, and applications. ACM SIGKDD Explor. Newsl. 24(2), 32–47 (2022)
Jin, W., Ma, Y., Liu, X., Tang, X., Wang, S., Tang, J.: Graph structure learning for robust graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 66–74 (2020)
Jin, W., Zhao, T., Ding, J., Liu, Y., Tang, J., Shah, N.: Empowering graph representation learning with test-time graph transformation. arXiv preprint arXiv:2210.03561 (2022)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Liu, J., Xie, C., Kenthapadi, K., Koyejo, S., Li, B.: RVFR: robust vertical federated learning via feature subspace recovery. In: NeurIPS Workshop New Frontiers in Federated Learning: Privacy, Fairness, Robustness, Personalization and Data Ownership (2021)
Liu, J., Xie, C., Koyejo, S., Li, B.: CoPur: certifiably robust collaborative inference via feature purification. Adv. Neural. Inf. Process. Syst. 35, 26645–26657 (2022)
Liu, R., Xing, P., Deng, Z., Li, A., Guan, C., Yu, H.: Federated graph neural networks: Overview, techniques and challenges. arXiv preprint arXiv:2202.07256 (2022)
Liu, Y., et al.: Vertical federated learning. arXiv:2211.12814 (2022)
Liu, Y., Yi, Z., Chen, T.: Backdoor attacks and defenses in feature-partitioned collaborative learning. arXiv preprint arXiv:2007.03608 (2020)
McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Inf. Retrieval 3, 127–163 (2000)
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93–93 (2008)
Wu, B., et al.: A survey of trustworthy graph learning: reliability, explainability, and privacy protection. arXiv preprint arXiv:2205.10014 (2022)
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., Weinberger, K.: Simplifying graph convolutional networks. In: International conference on machine learning, pp. 6861–6871. PMLR (2019)
Zhang, S., Chen, H., Sun, X., Li, Y., Xu, G.: Unsupervised graph poisoning attack via contrastive loss back-propagation. In: Proceedings of the ACM Web Conference 2022, pp. 1322–1330 (2022)
Zügner, D., Akbarnejad, A., Günnemann, S.: Adversarial attacks on neural networks for graph data. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2847–2856 (2018)
Acknowledgement
This work was supported by Natural Science Foundation of China (62272403, 61872306), and FuXiaQuan National Independent Innovation Demonstration Zone Collaborative Innovation Platform (No.3502ZCQXT2021003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Yang, Z., Fan, X., Wang, Z., Wang, Z., Wang, C. (2024). A Robust Detection and Correction Framework for GNN-Based Vertical Federated Learning. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14427. Springer, Singapore. https://doi.org/10.1007/978-981-99-8435-0_8
Download citation
DOI: https://doi.org/10.1007/978-981-99-8435-0_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8434-3
Online ISBN: 978-981-99-8435-0
eBook Packages: Computer ScienceComputer Science (R0)