Federated heterogeneous graph contrastive network with fine-grained bidirectional knowledge distillation

Dandan LIU , Yawen LI , Zhe XUE , Aijing LI , Tong ZHAO , Wenling LI , Haisheng LI

Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (1) : 2101313

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Front. Comput. Sci. ›› 2027, Vol. 21 ›› Issue (1) :2101313 DOI: 10.1007/s11704-025-51239-6
Artificial Intelligence
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Federated heterogeneous graph contrastive network with fine-grained bidirectional knowledge distillation
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Dandan LIU, Yawen LI, Zhe XUE, Aijing LI, Tong ZHAO, Wenling LI, Haisheng LI. Federated heterogeneous graph contrastive network with fine-grained bidirectional knowledge distillation. Front. Comput. Sci., 2027, 21(1): 2101313 DOI:10.1007/s11704-025-51239-6

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References

[1]

Fu X, King I. FedHGN: a federated framework for heterogeneous graph neural networks. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence. 2023, 3705−3713

[2]

Li T, Sahu A K, Zaheer M, Sanjabi M, Talwalkar A, Smith V. Federated optimization in heterogeneous networks. In: Proceedings of the 3rd Conference on Machine Learning and Systems. 2020, 429−450

[3]

Wu Z, Li X, Zhu Y, Li R H, Wang G, Zhou C. Federated prototype graph learning. 2025, arXiv preprint arXiv: 2504.09493

[4]

Huang W, Wan G, Ye M, Du B. Federated graph semantic and structural learning. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence. 2023, 3830−3838

[5]

Li S, Guo J . Subgraph federated learning with information bottleneck constrained generative learning. ACM Transactions on Knowledge Discovery from Data, 2025, 19( 6): 124

[6]

Zhu Z, Hong J, Zhou J. Data-free knowledge distillation for heterogeneous federated learning. In: Proceedings of the 38th International Conference on Machine Learning. 2021, 12878−12889

[7]

Fu J, Li C, Zhao Z, Zeng Q . Heterogeneous graph knowledge distillation neural network incorporating multiple relations and cross-semantic interactions. Information Sciences, 2024, 658: 120004

[8]

Karypis G, Kumar V . A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 1998, 20( 1): 359–392

[9]

McMahan B, Moore E, Ramage D, Hampson S, Arcas B A Y. Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics. 2017, 1273−1282

[10]

Li X, Wu Z, Zhang W, Zhu Y, Li R H, Wang G . FedGTA: topology-aware averaging for federated graph learning. Proceedings of the VLDB Endowment, 2023, 17( 1): 41–50

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