PrivFR:隐私增强的联合推与共享哈希嵌入
IEEE transactions on neural networks and learning systems
|April 22, 2024
概括
联合推系统 (FRS) 现在可以更高效,并通过共享哈希嵌入处理新项目. 这种保护隐私的方法可以降低跨设备学习中的存储和通信成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 联合推系统 (FRS) 通过在分布式用户数据上训练模型来提供隐私优势.
- 传统的FRS面临的挑战是,由于大型模型传输和词汇之外的项目 (OOV) 问题,它们的碳足迹很高.
研究的目的:
- 为跨设备设置提出一个高效和增强隐私的联合推框架.
- 解决当前FRS在资源消耗和处理OOV项目方面的局限性.
主要方法:
- 介绍了PrivFR,这是一个利用共享哈希嵌入和多个哈希函数用于资源高效的项目表示的框架.
- 开发了一种机制,在本地客户端上维护一个小型共享哈希嵌入池,避免存储所有项目嵌入向量.
- 为当地客户提供数据隐私保护的理论证明.
主要成果:
- 与传统的FRS相比,PrivFR显著降低了存储和通信的开销.
- 拟议的方法有效地处理OOV项目问题在联合推场景中.
- 实验结果表明,PrivFR的性能优于现有的最先进的FRS.
结论:
- PrivFR提供了一种新的解决方案,用于高效和保护隐私的联合推.
- 共享哈希嵌入方法有效地减轻了资源限制,并解决了FRS中的OOV问题.
- 该框架提高了FRS在现实世界跨设备应用中的实用性和性能.
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