Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis
文献类型：期刊论文
| 作者 | Zeng, Xingjie5; Zhou, Tao5; Bao, Zhicheng5; Zhao, Hongwei5; Chen, Leiming5; Wang, Xiao2,3,4 ; Wang, Feiyue1
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| 刊名 | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
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| 出版日期 | 2022-12-29 |
| 页码 | 11 |
| ISSN号 | 2329-924X |
| 关键词 | Federated learning Data models Artificial intelligence Graph neural networks Training Learning systems Servers graph neural networks responsible artificial intelligence (AI) weight divergence weight similarity evaluation |
| DOI | 10.1109/TCSS.2022.3230987 |
| 通讯作者 | Zeng, Xingjie(zengxjupc@163.com) |
| 英文摘要 | Federated learning enables multiple clients to learn a general model without sharing local data, and the federated learning system also improves information security and advances responsible artificial intelligence (AI). However, the data of different clients in the system are non-independently and identically distributed (IID), which results in weight divergence, especially for complex graph data extraction. This article proposes a novel feature-contrastive graph federated (FcgFed) learning approach to improve the robustness of the federated learning system in graph data. First, we design an architecture for FcgFed learning systems to analyze graph information. Furthermore, we present a graph federated learning method based on contrastive learning to alleviate the weight divergence in federated learning. The experiments in node classification and graph classification demonstrate that our method achieves better performance than model-contrastive federated learning (MOON) and federated average (FedAvg). We also test the adaptability of our method in image classification, and the results demonstrate that weight similarity evaluation works for other frameworks and tasks. |
| WOS关键词 | NEURAL-NETWORKS |
| 资助项目 | National Natural Science Foundation of China[62072469] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000910550800001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51063]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Zeng, Xingjie |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 230031, Peoples R China 4.Anhui Univ, Sch Artificial Intelligence e, Hefei 266114, Peoples R China 5.China Univ Petr East China, Dept Coll Comp Sci & Technol, Qingdao 266580, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zeng, Xingjie,Zhou, Tao,Bao, Zhicheng,et al. Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2022:11. |
| APA | Zeng, Xingjie.,Zhou, Tao.,Bao, Zhicheng.,Zhao, Hongwei.,Chen, Leiming.,...&Wang, Feiyue.(2022).Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,11. |
| MLA | Zeng, Xingjie,et al."Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2022):11. |
入库方式： OAI收割
来源：自动化研究所
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