Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator

• DOI: 10.48550/arxiv.1911.09824
• Publication Date: 2019-01-01
• Authors: Shengwen Yang; Bing Ren; Chao Zhou; Liping Liu
• Keywords: Federated Learning; Transfer of learning; Distributed learning; Distributed database

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