As data privacy is gradually valued by people, federated learning(FL) has emerged because of its potential to protect data. FL uses homomorphic encryption and differential privacy encryption on the promise of ensuring data security to realize distributed machine learning by exchanging encrypted information between different data providers. However, there are still many problems in FL, such as the communication efficiency between the client and the server and the data is non-iid. In order to solve the two problems mentioned above, we propose a novel vertical federated learning framework based on the DFP and the BFGS(denoted as BDFL), then apply it to logistic regression. Finally, we perform experiments using real datasets to test efficiency of BDFL framework.

翻译：由于人们逐渐重视数据隐私,联合学习(FL)因其保护数据的潜力而出现。FL利用同质加密和有区别的隐私加密来保证数据安全,通过在不同数据提供者之间交换加密信息实现分布式机器学习。然而,在FL中仍然存在许多问题,例如客户与服务器之间的通信效率,而且数据是非二元的。为解决上述两个问题,我们提议以DFP和BFGS(称为BDFL)为基础,建立一个新的纵向联合学习框架,然后将其应用于后勤回归。最后,我们利用实际数据集进行实验,以测试BDFL框架的效率。