邻居的回声:个人化私人联合学习的隐私放大与混合模型
Yixuan Liu1,2,3, Suyun Zhao1,2,3, Li Xiong4
1Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education, Renmin University of China.
概括
联邦学习 (FL) 隐私使用APES框架来增强,该框架利用混合来实现更强大的中央隐私,同时保持个性化的本地隐私. 这种方法提高了合作培训的模型准确性和隐私保证.
科学领域:
- 计算机科学 计算机科学
- 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) 能够实现协作模式培训,但面临隐私漏洞.
- 个性化本地差异隐私 (PLDP) 提供了不同的用户隐私,但中央保障较弱.
- 在佛罗里达州,平衡个性化的本地隐私与强大的中央隐私是一个重大挑战.
研究的目的:
- 根据PLDP,制定一个通用框架 (APES),以加强FL的中央隐私.
- 量化和利用不同类型的用户对中央隐私的贡献.
- 为高维情景引入一个精细的框架 (S-APES).
主要方法:
- 在一般框架 (APES) 中利用混合模型的隐私放大效应.
- 使用邻居分歧和剪贴替代机制量化用户对中央隐私的贡献.
- 在高维数据的S-APES框架中应用后分散.
主要成果:
- 通过利用混合模型效应,APES加强了PLDP下的中央隐私保证.
- 不同类型的用户对隐私的贡献被量化,从而导致更严格的隐私限制.
- 在高维的FL设置中,S-APES有效地减少了隐私损失.
- 这项研究是首次分析混动对个性化本地隐私的影响.
结论:
- 拟议的APES和S-APES框架提供了一种新的方法来加强联邦学习中的隐私.
- 这些方法可以实现更强大的中央隐私,同时保持个性化的本地隐私和模型实用性.
- 实验结果验证了拟议框架在提高隐私和准确性的有效性.
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