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 of many participants. To relax their privacy concerns about ID privacy, this paper formally proposes the notion of asymmetrically vertical federated learning and illustrates the way to protect sample IDs. The standard private set intersection protocol is adapted to achieve the asymmetric ID alignment phase in an asymmetrically 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 asymmetric 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.

本文提出新的不对称垂直联邦学习架构来保护数据隐私，采用标准的私有集合交集协议和Pohlig-Hellman方法来实现不对称ID对齐，并提供真实和虚假的方法来实现联邦模型训练。通过实验证明了该方法的可行性。

非对称垂直联邦学习