相关实验视频

FedDyH:一个多方策略与GA优化框架,用于动态异质联合学习.

Xuhua Zhao1, Yongming Zheng2, Jiaxiang Wan3

  • 1School of Electronic Information, Zhejiang Guangsha Vocational and Technical University of Construction, Dongyang 322103, China.

Biomimetics (Basel, Switzerland)
|March 26, 2025
PubMed
概括

联合学习 (FL) 面临着动态数据异质性的挑战. 该 FedDyH 框架使用生物系统灵感,跨客户端蒸,自适应规范化和遗传算法来提高在各种环境中的模型稳定性和准确性.

关键词:
灾难性的遗忘.联合学习的联合学习遗传算法是一种遗传算法.知识的蒸知识的蒸.

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