论文标题
不对称垂直联合学习
Asymmetrical Vertical Federated Learning
论文作者
论文摘要
联合学习是一种分布式的机器学习方法，旨在保留样本特征和标签的隐私。在联合学习系统中，通常采用基于ID的样本一致性方法，而在保护ID隐私方面几乎没有努力。但是，在现实生活中，最强的行标识符的样本ID的机密性也引起了许多参与者的关注。为了放松他们对ID隐私的隐私问题，本文正式提出了不对称垂直联合学习的概念，并说明了保护样本ID的方式。标准的私有集体交叉协议适应以在不对称的垂直联合学习系统中实现不对称的ID对齐阶段。相应地，提供了适应协议的Pohlig-Hellman实现。本文还提供了一种真正的虚拟方法来实现不对称联合模型培训的方法。为了说明其应用，作为一个例子，提供了联合逻辑回归算法。还进行了实验以验证这种方法的可行性。
Federated learning is a distributed machine learning method that aims to preserve the privacy of sample features and labels. In a federated learning system, ID-based sample alignment approaches are usually applied with few efforts made on the protection of ID privacy. In real-life applications, however, the confidentiality of sample IDs, which are the strongest row identifiers, is also drawing much attention from many participants. To relax their privacy concerns about ID privacy, this paper formally proposes the notion of asymmetrical vertical federated learning and illustrates the way to protect sample IDs. The standard private set intersection protocol is adapted to achieve the asymmetrical ID alignment phase in an asymmetrical vertical federated learning system. Correspondingly, a Pohlig-Hellman realization of the adapted protocol is provided. This paper also presents a genuine with dummy approach to achieving asymmetrical federated model training. To illustrate its application, a federated logistic regression algorithm is provided as an example. Experiments are also made for validating the feasibility of this approach.
