Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption
<|reference_start|>Efficient and Privacy-Preserving Federated Learning based on Full Homomorphic Encryption: 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.<|reference_end|>
@article{guo2024efficient,
    title={Efficient and Privacy-Preserving Federated Learning based on Full
  Homomorphic Encryption},
    author={Yuqi Guo, Lin Li, Zhongxiang Zheng, Hanrui Yun, Ruoyan Zhang, Xiaolin
  Chang, Zhixuan Gao},
    journal={arXiv preprint arXiv:2403.11519},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11519},
    primaryClass={cs.CR}
}