  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.
