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Updated: Jun 29, 2025

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关于深度学习与差异性隐私的融合和校准
Zhiqi Bu1, Hua Wang1, Zongyu Dai1
1University of Pennsylvania.
概括
不同隐私 (DP) 培训影响模型的融合和校准. 梯度剪切会影响收,而增加噪音会影响隐私风险,而不是校准.
科学领域:
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
- 深度神经网络 深度神经网络
背景情况:
- 不同隐私 (DP) 培训可以提高数据隐私,但往往会降低模型的准确性和校准.
- 了解DP培训的融合动态对于实际应用至关重要.
研究的目的:
- 使用连续时间框架分析DP培训的融合和校准.
- 在DP培训中调查梯度剪切和噪声添加的独特影响.
主要方法:
- 通过神经触角内核 (NTK) 制定了连续时间分析.
- 在DP训练中,特征为每个样本的梯度剪切 (平面和层次) 和噪声添加.
- 检查了任意的网络架构和丢失函数.
主要成果:
- 增加噪音会影响隐私风险,但不会影响收或校准.
- 每个样本的梯度剪切影响了合和校准.
- 小切割规范产生更高的精度,但校准较差;大切割规范提供类似的精度与改进的校准.
结论:
- 梯度剪切是影响DP模型融合和校准的主要因素.
- 选择合适的剪切标准对于平衡DP模型中的隐私,准确性和可靠性至关重要.
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