相关实验视频

顺序-最佳的拜占庭-强大的学习通过公平的梯度剪切在异质性下

Zhi-Yong Wang, Hao Nan Sheng, Qiushi Yang

    IEEE transactions on cybernetics
    |November 11, 2025
    PubMed
    概括

    这项研究介绍了一种新的渐变剪切方法,用于拜占庭-强大的联合学习 (FL). 新的聚合规则提高了机器学习 (ML) 模型的趋同性和准确性,即使使用异质数据,也优于现有方法.

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