LF3PFL:

Yong Li1,2,3, Gaochao Xu1, Xutao Meng2

  • 1School of Computer Science and Technology, Jilin University, Changchun 130012, China.

PubMed
概括

本地化联合更新 (LF3PFL) 在不牺牲性能的情况下增强联合学习中的隐私. 这种新的方法提高了数据保密性和模型有效性,为安全的机器学习提供了实际解决方案.

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