Since the first theoretically feasible full homomorphic encryption (FHE) scheme was proposed in 2009, great progress has been achieved. These improvements have made FHE schemes come off the paper and become quite useful in solving some practical problems. In this paper, we propose a set of novel Federated Learning Schemes by utilizing the latest homomorphic encryption technologies, so as to improve the security, functionality and practicality at the same time. Comparisons have been given in four practical data sets separately from medical, business, biometric and financial fields, covering both horizontal and vertical federated learning scenarios. The experiment results show that our scheme achieves significant improvements in security, efficiency and practicality, compared with classical horizontal and vertical federated learning schemes.

翻译：自2009年首个理论上可行的全同态加密方案提出以来，该领域取得了长足进展。这些改进使全同态加密方案从理论走向实践，在解决实际问题上展现出显著价值。本文利用最新同态加密技术提出一系列新型联邦学习方案，旨在同步提升安全性、功能性与实用性。基于涵盖医疗、商业、生物特征及金融领域的四组实际数据集，我们分别针对横向联邦学习与纵向联邦学习场景进行了对比实验。结果表明，与经典横向和纵向联邦学习方案相比，所提方案在安全性、效率与实用性方面均实现了显著提升。