基于两阶段梯度修剪和差异化差异化隐私的高效通信,保护隐私的联合学习算法
Yong Li1,2,3, Wei Du1, Liquan Han1
1School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了IsmDP-FL,这是一种新的联合学习 (FL) 算法,可以提高通信效率并保持模型隐私. 它使用双阶段梯度修剪和差异化的差异隐私来降低成本和保护敏感数据.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 面临着安全和高通讯成本的挑战.
- 不同隐私 (DP) 保护数据,但可以降低模型的准确性.
- 现有的模型修剪方法旨在减少通讯开销.
研究的目的:
- 开发一种高效的通信和保护隐私的FL算法.
- 为了解决隐私保护和模型准确性之间的权衡问题,FL.
- 为了减少在训练大规模联合模型的通信负担.
主要方法:
- 介绍了IsmDP-FL,这是一个两阶段的联合学习算法.
- 采用了梯度修剪和差异化的差异隐私.
- 应用DP在修剪后的重要参数和网络层中的剩余参数.
主要成果:
- 在广泛的实验中证明了高的通信效率.
- 在整个联合学习过程中成功维护模型隐私.
- 减少了隐私预算的不必要消耗.
结论:
- 在联合学习中,IsmDP-FL有效地平衡了隐私和沟通效率.
- 拟议的方法为安全和高效的大规模模型培训提供了一个实际的解决方案.
- 这种方法最大限度地减少了隐私预算的使用,而不会影响模型性能.
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