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

Abstract

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.

IJCAI-19 Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (IJCAI (FML)) 2019

References in corpus (2)

• Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption

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