LF3PFL:基于地方联邦化方案的实用隐私保护联合学习算法
Yong Li1,2,3, Gaochao Xu1, Xutao Meng2
1School of Computer Science and Technology, Jilin University, Changchun 130012, China.
Entropy (Basel, Switzerland)
|May 24, 2024
概括
本地化联合更新 (LF3PFL) 在不牺牲性能的情况下增强联合学习中的隐私. 这种新的方法提高了数据保密性和模型有效性,为安全的机器学习提供了实际解决方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 联合学习 (FL) 引发了由于模型数据交换的隐私问题.
- 现有的隐私方法,如差异隐私 (DP) 和安全的多方计算 (SMC),具有性能和实施挑战.
研究的目的:
- 提出和评估一种新的,务实的方法来保护FL的隐私.
- 通过本地化联合更新 (LF3PFL) 增强参与者数据保护和模型有效性.
主要方法:
- 开发并实施本地化联合更新 (LF3PFL) 方法.
- 集成的交叉优化,微调和信息损失减少.
- 在CIFAR-10,莎士比亚和MNIST数据集的理论和实证验证,使用了五种本地模型 (简单-CNN,中度CNN,Lenet,VGG9,Resnet18).
主要成果:
- 在各种模型和数据集中,LF3PFL保持了具有竞争力的培训准确性.
- 与最先进的技术相比,在隐私保护方面取得了显著的改进.
- 在模型性能和数据保密性之间实现了强大的平衡.
结论:
- LF3PFL提供了一个可扩展和有效的解决方案,用于保护联邦学习中的隐私.
- 本地化联合更新是未来FL隐私策略的一个有希望的关键组成部分.
- 该方法解决了实际挑战,提高了FL应用程序的安全性和可用性.
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