在联合学习中,ALDP-FL支持适应性局部差异隐私
1College of Computers Science and Cyber Security, Chengdu University of Technology, Chengdu, 610059, China.
Scientific reports
|July 22, 2025
概括
联合学习隐私通过新的自适应本地化差异隐私联合学习 (ALDP-FL) 方法得到了增强. 这种方法将自适应噪声注入模型更新中,显著提高准确性并保护敏感的用户数据免受推断攻击.
科学领域:
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
- 网络安全 网络安全
背景情况:
- 联合学习 (FL) 在不共享原始用户信息的情况下,在分散的数据上训练模型.
- 然而,FL很容易受到隐私攻击,攻击者可以从模型更新中推断出敏感数据.
- 现有的隐私保护方法在FL中经常难以平衡隐私保证与模型实用性.
研究的目的:
- 提出一种新的保护隐私的联合学习方法,即自适应本地化差异隐私联合学习 (ALDP-FL).
- 通过在模型更新中动态注入噪音来增强联合学习中的数据隐私.
- 保持高模型准确性,同时提供强大的保护,防止敏感信息推断.
主要方法:
- ALDP-FL根据其标准的移动平均值动态调整每个网络层的剪切值.
- 适应性噪声被注入到每个层,根据更新的特定特征量身定制.
- 使用边界扰动机制来减轻增加噪声引起的精度下降.
主要成果:
- 在所有关键指标上,ALDP-FL表现出显著的改善:准确度 (+10.57%),精度 (+10.64%),回忆力 (+10.52%) 和F1评分 (+10.64%).
- 该方法在防御iDIG攻击重建方面表现出卓越的性能,MSE改善了391.2%,SSIM改善了-85.4%.
- 在MNIST,时尚MNIST和CIFAR-10数据集上的实验验证实了ALDP-FL的有效性和实用性.
结论:
- ALDP-FL有效地提高了联邦学习中的隐私,而不会大幅损失模型性能.
- 适应式噪声注入和边界扰动机制为保护隐私的机器学习提供了强大的解决方案.
- 拟议的方法在保护用户数据免受复杂的推理攻击方面显著优于现有技术.
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