不同的私有知识转移为联合学习的联邦学习
Tao Qi1, Fangzhao Wu2, Chuhan Wu3
1Department of Electronic Engineering, Tsinghua University, 100084, Beijing, China.
Nature communications
|June 24, 2023
概括
联合学习共享模型参数,而不是原始数据,以保护隐私. 一种名为PrivateKT的新方法利用公共数据进行安全有效的知识传输,大大缩小了绩效差距.
科学领域:
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
- 人工智能的人工智能
背景情况:
- 联合学习可以实现分散的数据分析,但可能会通过模型参数泄露隐私.
- 现有的联合学习方法努力保持隐私,同时实现高性能.
研究的目的:
- 推出PrivateKT,一种用于联合学习的新型知识传递方法.
- 在知识转移期间使用积极选择的公共数据确保隐私保证.
- 在严格的隐私约束下,提高联合学习的性能.
主要方法:
- 开发了PrivateKT,这是一个知识转移框架,利用小的,积极选择的公共数据集.
- 实施了不同的隐私机制来保护敏感信息.
- 在三个不同的数据集上对PrivateKT进行了评估.
主要成果:
- 私人KT显著减少了集中式和联合式学习之间的绩效差距.
- 在严格的差异隐私下,在绩效差距中实现了高达84%的减少.
- 证明有效的知识转移与强大的隐私保护.
结论:
- 在机器学习中,PrivateKT为有效和保护隐私的知识转移提供了一个有前途的方向.
- 该方法可以从分散的,对隐私敏感的数据中提取高质量的知识.
- 解决了在联合系统中平衡性能和隐私的关键挑战.
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