MatSwarm:可信的群体传输学习驱动的材料计算,用于安全的大数据共享
Ran Wang1,2,3, Cheng Xu4,5,6, Shuhao Zhang3
1School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China.
Nature communications
|October 29, 2024
概括
MatSwarm是一个新的框架,集成了联合学习和区块链,增强了协作材料研究. 它提高了模型准确性和数据安全性,克服了工业4.0材料开发中的挑战.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 工业4.0推动了对新材料的需求,需要机构间的合作.
- 数据仓库和非识别的数据库. 在多机构设置中的数据阻碍了协作模型的准确性.
- 保护敏感数据是协作材料研究的一个主要挑战.
研究的目的:
- 引入MatSwarm框架,用于安全和准确的协作材料数据分析.
- 为了应对数据异质性的挑战,非i.i.d. 数据,以及材料研究中的数据保密性.
- 在材料科学的联合学习环境中增强模型的概括性和准确性.
主要方法:
- 开发了MatSwarm框架,结合了群体学习,联合学习和区块链技术.
- 实施了一种群体转移学习方法,用于参数对齐的规范化术语.
- 使用Intel SGX的可信执行环境 (TEE) 来增强数据安全性和保密性.
主要成果:
- 马特斯瓦姆成功地汇总了来自30多个机构的1400多万个物质数据条目.
- 与独立训练的模型相比,该框架显示出更高的准确性和概括性.
- 在整个模型培训和聚合过程中确保数据保密.
结论:
- 在协作材料研究中,MatSwarm有效地克服了数据孤岛和安全挑战.
- 该框架显著提高了材料数据分析的准确性和概括性.
- MatSwarm促进了安全和高效的多机构合作,以加速材料发现.
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