加强分区区分:与推忘记学习相反的政策
Lin Li1, Shengda Zhuo1, Hongguang Lin1
1College of Cyber Security, Jinan University, Guangzhou 511443, Guangdong, China.
概括
本研究引入了分区区分与对比推取消学习 (PDCRU),以解决推系统中的隐私问题. PDCRU平衡了数据分区和特征稀疏性,提高了推取消学习的效率和性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 在推系统中,越来越多的隐私和数据污染问题需要有效的推取消学习方法.
- 现有的方法往往忽视了去学习的完整性,实用性和效率之间的平衡,阻碍了现实世界的应用.
- 之前的研究受到了不平衡的本地和全球协作信息和数据稀疏性问题,这些问题因分区而加剧.
研究的目的:
- 提出一种新的数据分区和子模型培训策略,PDCRU,用于推取消学习.
- 通过平衡数据分区和特征稀疏度来解决现有方法的局限性.
- 增强推系统的学习和取消学习能力.
主要方法:
- 开发了分区区分与对比性推取消学习 (PDCRU) 策略.
- 提取的结构特征作为样本的全球协作信息.
- 引入了基于分区期间样本相似性的结构特征约束.
- 在子模型培训中利用对比式学习来增强模型嵌入.
主要成果:
- PDCRU证明了可行性,并且在现有的推取消学习模型上始终保持优势.
- 在推表现方面取得了4.83%的改善.
- 与基线方法相比,建议取消学习效率提高了4.64倍.
结论:
- PDCRU有效地平衡了数据分区和功能稀疏性在推取消学习.
- 拟议的方法在推质量和消除学习效率方面都提供了显著的改进.
- PDCRU为保护隐私和高效推系统提供了一个有希望的解决方案.
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