Knowledge Commons of Institute of Automation,CAS
Feature-Contrastive Graph Federated Learning: Responsible AI in Graph Information Analysis | |
Zeng, Xingjie1; Zhou, Tao1; Bao, Zhicheng1; Zhao, Hongwei1; Chen, Leiming1; Wang, Xiao2,3,4; Wang, Feiyue5 | |
发表期刊 | IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS |
ISSN | 2329-924X |
2022-12-29 | |
页码 | 11 |
通讯作者 | Zeng, Xingjie(zengxjupc@163.com) |
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. |
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 |
关键词[WOS] | NEURAL-NETWORKS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62072469] |
项目资助者 | National Natural Science Foundation of China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Cybernetics ; Computer Science, Information Systems |
WOS记录号 | WOS:000910550800001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51064 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Zeng, Xingjie |
作者单位 | 1.China Univ Petr East China, Dept Coll Comp Sci & Technol, Qingdao 266580, Peoples R China 2.Anhui Univ, Sch Artificial Intelligence e, Hefei 266114, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 230031, Peoples R China 4.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Inst Automat, Beijing 100190, 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. |
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