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基于茫然传输协议的FATE联邦迁移学习方案 - 中国知网
Source: kns.cnki.net
Article summary:
1. This article proposes a secure matrix computation scheme based on the Oblivious Transfer Protocol for FATE federated transfer learning.
2. The proposed scheme enables secure machine learning model training with data privacy protection, and is more efficient than existing schemes based on homomorphic encryption.
3. Performance analysis shows that the proposed scheme is secure and private, and has a certain degree of scalability, reducing the average convergence time by 25% compared to homomorphic encryption-based schemes.
Article analysis:
The article is generally trustworthy and reliable in its content. It provides an overview of the proposed scheme, as well as detailed performance analysis results to support its claims. The article does not appear to be biased or one-sided in its reporting, as it presents both sides of the argument fairly and objectively. Furthermore, it does not contain any promotional content or partiality towards any particular viewpoint or opinion. All possible risks are noted in the article, such as potential privacy issues related to collecting large amounts of data for machine learning models. Additionally, all claims made are supported by evidence from experiments conducted using real datasets.
The only potential issue with this article is that it does not explore counterarguments or alternative solutions to the problem being addressed. However, this does not significantly detract from its overall trustworthiness and reliability since it provides a comprehensive overview of the proposed solution and its performance analysis results.