Federated Subgraph Learning via Global-Knowledge-Guided Node Generation
Federated graph learning (FGL) is a combination of graph representation learning and federated learning that utilizes graph neural networks (GNNs) to process complex graph-structured data while addressing data silo issues. However, during the local training of GNNs, each client only has access to a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2240 |
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| author | Yuxuan Liu Zhiming He Shuang Wang Yangyang Wang Peichao Wang Zhangshen Huang Qi Sun |
| author_facet | Yuxuan Liu Zhiming He Shuang Wang Yangyang Wang Peichao Wang Zhangshen Huang Qi Sun |
| author_sort | Yuxuan Liu |
| collection | DOAJ |
| description | Federated graph learning (FGL) is a combination of graph representation learning and federated learning that utilizes graph neural networks (GNNs) to process complex graph-structured data while addressing data silo issues. However, during the local training of GNNs, each client only has access to a subgraph, significantly deteriorating performance. To address this issue, recent solutions propose completing the subgraph with pseudo graph nodes generated by a generator trained using the local subgraph. Despite their effectiveness, such methods may introduce biases as the local pseudo graph nodes cannot accurately represent the global graph distribution. To overcome this problem, we introduce MN-FGAGN, which mitigates the impact of missing neighbor information by generating pseudo graph nodes that follow the global distribution. The main idea of our approach is to partition the generative adversarial neural network into a client-side discriminator and a server-side generator. In this way, the generator can receive supervised information from all clients and can thus generate graph nodes that contain global information. Experiments on four real-world graph datasets show that it outperforms the state-of-the-art methods. |
| format | Article |
| id | doaj-art-638ba76f7b2e44adb05f6a6725433ffb |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-638ba76f7b2e44adb05f6a6725433ffb2025-08-20T03:03:27ZengMDPI AGSensors1424-82202025-04-01257224010.3390/s25072240Federated Subgraph Learning via Global-Knowledge-Guided Node GenerationYuxuan Liu0Zhiming He1Shuang Wang2Yangyang Wang3Peichao Wang4Zhangshen Huang5Qi Sun6School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaHangzhou Nuowei Information Technology Company Ltd., Hangzhou 310059, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaHangzhou Nuowei Information Technology Company Ltd., Hangzhou 310059, ChinaFederated graph learning (FGL) is a combination of graph representation learning and federated learning that utilizes graph neural networks (GNNs) to process complex graph-structured data while addressing data silo issues. However, during the local training of GNNs, each client only has access to a subgraph, significantly deteriorating performance. To address this issue, recent solutions propose completing the subgraph with pseudo graph nodes generated by a generator trained using the local subgraph. Despite their effectiveness, such methods may introduce biases as the local pseudo graph nodes cannot accurately represent the global graph distribution. To overcome this problem, we introduce MN-FGAGN, which mitigates the impact of missing neighbor information by generating pseudo graph nodes that follow the global distribution. The main idea of our approach is to partition the generative adversarial neural network into a client-side discriminator and a server-side generator. In this way, the generator can receive supervised information from all clients and can thus generate graph nodes that contain global information. Experiments on four real-world graph datasets show that it outperforms the state-of-the-art methods.https://www.mdpi.com/1424-8220/25/7/2240federated learningdistributed learninggraph learningmachine learningdeep learning |
| spellingShingle | Yuxuan Liu Zhiming He Shuang Wang Yangyang Wang Peichao Wang Zhangshen Huang Qi Sun Federated Subgraph Learning via Global-Knowledge-Guided Node Generation Sensors federated learning distributed learning graph learning machine learning deep learning |
| title | Federated Subgraph Learning via Global-Knowledge-Guided Node Generation |
| title_full | Federated Subgraph Learning via Global-Knowledge-Guided Node Generation |
| title_fullStr | Federated Subgraph Learning via Global-Knowledge-Guided Node Generation |
| title_full_unstemmed | Federated Subgraph Learning via Global-Knowledge-Guided Node Generation |
| title_short | Federated Subgraph Learning via Global-Knowledge-Guided Node Generation |
| title_sort | federated subgraph learning via global knowledge guided node generation |
| topic | federated learning distributed learning graph learning machine learning deep learning |
| url | https://www.mdpi.com/1424-8220/25/7/2240 |
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