MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing
Abstract The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additional...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-10-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-53431-x |
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| _version_ | 1850179531599511552 |
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| author | Ran Wang Cheng Xu Shuhao Zhang Fangwen Ye Yusen Tang Sisui Tang Hangning Zhang Wendi Du Xiaotong Zhang |
| author_facet | Ran Wang Cheng Xu Shuhao Zhang Fangwen Ye Yusen Tang Sisui Tang Hangning Zhang Wendi Du Xiaotong Zhang |
| author_sort | Ran Wang |
| collection | DOAJ |
| description | Abstract The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additionally, the heterogeneous and non-independent and identically distributed (non-i.i.d.) nature of material data hinders model accuracy and generalization in collaborative computing. In this paper, we introduce the MatSwarm framework, built on swarm learning, which integrates federated learning with blockchain technology. MatSwarm features two key innovations: a swarm transfer learning method with a regularization term to enhance the alignment of local model parameters, and the use of Trusted Execution Environments (TEE) with Intel SGX for heightened security. These advancements significantly enhance accuracy, generalization, and ensure data confidentiality throughout the model training and aggregation processes. Implemented within the National Material Data Management and Services (NMDMS) platform, MatSwarm has successfully aggregated over 14 million material data entries from more than thirty research institutions across China. The framework has demonstrated superior accuracy and generalization compared to models trained independently by individual institutions. |
| format | Article |
| id | doaj-art-28246a154c38461eb50c0ffed045e320 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-28246a154c38461eb50c0ffed045e3202025-08-20T02:18:28ZengNature PortfolioNature Communications2041-17232024-10-0115111410.1038/s41467-024-53431-xMatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharingRan Wang0Cheng Xu1Shuhao Zhang2Fangwen Ye3Yusen Tang4Sisui Tang5Hangning Zhang6Wendi Du7Xiaotong Zhang8School of Computer and Communication Engineering, University of Science and Technology BeijingSchool of Computer and Communication Engineering, University of Science and Technology BeijingCollege of Computing and Data Science, Nanyang Technological UniversitySchool of Computer and Communication Engineering, University of Science and Technology BeijingSchool of Computer and Communication Engineering, University of Science and Technology BeijingSchool of Computer and Communication Engineering, University of Science and Technology BeijingSchool of Computer and Communication Engineering, University of Science and Technology BeijingSchool of Computer and Communication Engineering, University of Science and Technology BeijingSchool of Computer and Communication Engineering, University of Science and Technology BeijingAbstract The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additionally, the heterogeneous and non-independent and identically distributed (non-i.i.d.) nature of material data hinders model accuracy and generalization in collaborative computing. In this paper, we introduce the MatSwarm framework, built on swarm learning, which integrates federated learning with blockchain technology. MatSwarm features two key innovations: a swarm transfer learning method with a regularization term to enhance the alignment of local model parameters, and the use of Trusted Execution Environments (TEE) with Intel SGX for heightened security. These advancements significantly enhance accuracy, generalization, and ensure data confidentiality throughout the model training and aggregation processes. Implemented within the National Material Data Management and Services (NMDMS) platform, MatSwarm has successfully aggregated over 14 million material data entries from more than thirty research institutions across China. The framework has demonstrated superior accuracy and generalization compared to models trained independently by individual institutions.https://doi.org/10.1038/s41467-024-53431-x |
| spellingShingle | Ran Wang Cheng Xu Shuhao Zhang Fangwen Ye Yusen Tang Sisui Tang Hangning Zhang Wendi Du Xiaotong Zhang MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing Nature Communications |
| title | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing |
| title_full | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing |
| title_fullStr | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing |
| title_full_unstemmed | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing |
| title_short | MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing |
| title_sort | matswarm trusted swarm transfer learning driven materials computation for secure big data sharing |
| url | https://doi.org/10.1038/s41467-024-53431-x |
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