This article presents MatSwarm, a framework designed to enable secure, collaborative material science research by combining federated learning, blockchain, and trusted execution environments (TEE). MatSwarm addresses challenges like data security, heterogeneous datasets, and limited generalization in traditional models by ensuring secure local data processing and parameter sharing, without revealing sensitive information. Validated on China’s National Material Data Management platform, MatSwarm achieved high accuracy across diverse materials, showing strong scalability, security against data poisoning, and adaptability to varying datasets and participant numbers, advancing collaborative material innovation.
For more details, please continue reading the full article under the following link:
https://www.nature.com/articles/s41467-024-53431-x
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