With the development of laws and regulations related to privacy preservation, it has become difficult to collect personal data to perform machine learning. In this context, federated learning, which is distributed learning without sharing personal data, has been proposed. In this paper, we focus on federated learning for user authentication. We show that it is difficult to achieve both privacy preservation and high accuracy with existing methods. To address these challenges, we propose IPFed which is privacy-preserving federated learning using random projection for class embedding. Furthermore, we prove that IPFed is capable of learning equivalent to the state-of-the-art method. Experiments on face image datasets show that IPFed can protect the privacy of personal data while maintaining the accuracy of the state-of-the-art method.

翻译：随着隐私保护相关法律法规的发展，收集个人数据进行机器学习变得困难。在此背景下，联邦学习这种无需共享个人数据的分布式学习方式被提出。本文聚焦于用户身份认证的联邦学习，我们指出现有方法难以同时实现隐私保护与高精度。为解决这些问题，我们提出IPFed——一种使用随机投影进行类别嵌入的隐私保护联邦学习。此外，我们证明IPFed能够学习到与现有最优方法等价的效果。在人脸图像数据集上的实验表明，IPFed在保持与最优方法同等精度的同时，能够保护个人数据的隐私。