Communication Efficient and Differentially Private Logistic Regression under the Distributed Setting

We study the classic machine learning problem of logistic regression with differential privacy (DP), under the distributed setting. While logistic regression with DP has been extensively studied in the literature, most of the research is focused on the centralized setting, where a centralized server is trusted with the entire private training dataset. However, in many real-world scenarios (e.g., federated learning), the data is distributed among multiple clients who may not trust others, including clients and the server. While the server tries to learn a model using the clients' private datasets, the clients should provide each individual record in their local datasets with a formal privacy guarantee.