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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2507.19964 |
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| _version_ | 1866916865096286208 |
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| author | Li, Kunhao Wu, Di Bai, Jun Xu, Jing Yang, Lei Zhang, Ziyi Song, Yiliao Yang, Wencheng Cai, Taotao Li, Yan |
| author_facet | Li, Kunhao Wu, Di Bai, Jun Xu, Jing Yang, Lei Zhang, Ziyi Song, Yiliao Yang, Wencheng Cai, Taotao Li, Yan |
| contents | Graph-structured data is prevalent in many real-world applications, including social networks, financial systems, and molecular biology. Graph Neural Networks (GNNs) have become the de facto standard for learning from such data due to their strong representation capabilities. As GNNs are increasingly deployed in federated learning (FL) settings to preserve data locality and privacy, new privacy threats arise from the interaction between graph structures and decentralized training. In this paper, we present the first systematic study of cross-client membership inference attacks (CC-MIA) against node classification tasks of federated GNNs (FedGNNs), where a malicious client aims to infer which client owns the given data. Unlike prior centralized-focused work that focuses on whether a sample was included in training, our attack targets sample-to-client attribution, a finer-grained privacy risk unique to federated settings. We design a general attack framework that exploits FedGNNs' aggregation behaviors, gradient updates, and embedding proximity to link samples to their source clients across training rounds. We evaluate our attack across multiple graph datasets under realistic FL setups. Results show that our method achieves high performance on both membership inference and ownership identification. Our findings highlight a new privacy threat in federated graph learning-client identity leakage through structural and model-level cues, motivating the need for attribution-robust GNN design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19964 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks Li, Kunhao Wu, Di Bai, Jun Xu, Jing Yang, Lei Zhang, Ziyi Song, Yiliao Yang, Wencheng Cai, Taotao Li, Yan Machine Learning Graph-structured data is prevalent in many real-world applications, including social networks, financial systems, and molecular biology. Graph Neural Networks (GNNs) have become the de facto standard for learning from such data due to their strong representation capabilities. As GNNs are increasingly deployed in federated learning (FL) settings to preserve data locality and privacy, new privacy threats arise from the interaction between graph structures and decentralized training. In this paper, we present the first systematic study of cross-client membership inference attacks (CC-MIA) against node classification tasks of federated GNNs (FedGNNs), where a malicious client aims to infer which client owns the given data. Unlike prior centralized-focused work that focuses on whether a sample was included in training, our attack targets sample-to-client attribution, a finer-grained privacy risk unique to federated settings. We design a general attack framework that exploits FedGNNs' aggregation behaviors, gradient updates, and embedding proximity to link samples to their source clients across training rounds. We evaluate our attack across multiple graph datasets under realistic FL setups. Results show that our method achieves high performance on both membership inference and ownership identification. Our findings highlight a new privacy threat in federated graph learning-client identity leakage through structural and model-level cues, motivating the need for attribution-robust GNN design. |
| title | Who Owns This Sample: Cross-Client Membership Inference Attack in Federated Graph Neural Networks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.19964 |