  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.
