PEVLR: A New Privacy-Preserving and Efficient Approach for Vertical Logistic Regression

In our paper, we consider logistic regression in vertical federated learning. A new algorithm called PEVLR (Privacy-preserving and Efficient Vertical Logistic Regression) is proposed to efficiently solve vertical logistic regression with privacy preservation. To enhance the communication and computational efficiency, we design a novel local-update and global-update scheme for party \(\mathcal{A}\) and party \(\mathcal{B}\) , respectively. For the local update, we utilize hybrid SGD rather than vanilla SGD to mitigate the variance resulted from stochastic gradients. For the global update, full gradient is adopted to update the parameter of party \(\mathcal{B}\) , which leads to faster convergence rate and fewer communication rounds. Furthermore, we design a simple but efficient plan to exchange intermediate information with privacy-preserving guarantee. Specifically, random matrix sketch and random selected permutations are utilized to ensure the security of original data, label information and parameters under honest-but-curious assumption. The experiment results show the advantages of PEVLR in terms of convergence rate, accuracy and efficiency, compared with other related models.