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As data privacy is gradually valued by people, federated learning(FL) hasemerged because of its potential to protect data. FL uses homomorphicencryption and differential privacy encryption on the promise of ensuring datasecurity to realize distributed machine learning by exchanging encryptedinformation between different data providers. However, there are still manyproblems in FL, such as the communication efficiency between the client and theserver and the data is non-iid. In order to solve the two problems mentionedabove, we propose a novel vertical federated learning framework based on theDFP and the BFGS(denoted as BDFL), then apply it to logistic regression.Finally, we perform experiments using real datasets to test efficiency of BDFLframework.

Vertical federated learning based on DFP and BFGS