  Federated Learning is a new distributed learning mechanism which allows model
training on a large corpus of decentralized data owned by different data
providers, without sharing or leakage of raw data. According to the
characteristics of data dis-tribution, it could be usually classified into
three categories: horizontal federated learning, vertical federated learning,
and federated transfer learning. In this paper we present a solution for
parallel dis-tributed logistic regression for vertical federated learning. As
compared with existing works, the role of third-party coordinator is removed in
our proposed solution. The system is built on the pa-rameter server
architecture and aims to speed up the model training via utilizing a cluster of
servers in case of large volume of training data. We also evaluate the
performance of the parallel distributed model training and the experimental
results show the great scalability of the system.
