Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator
By Shengwen Yang, B. Ren, Liping Liu. 2019
147 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Science Applications
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qiang Yang
- Yang Liu
- Murali Annavaram
- Chaoyang He
- S. Avestimehr
- Tianjian Chen
- Praneeth Vepakomma
- Abhishek Singh
In Computer Science Applications
- Peilin Zhao
- Murali Annavaram
- Ramesh Raskar
- Qiang Yang
- Xiong Zhang
- Libin Wang
- Yang Liu
- Praneeth Vepakomma
Citations in the corpus
- SplitNN-driven Vertical Partitioning (2020, background): “The works in (Yang et al. , 2019a,b) provide schemes for secure vertically partitioned logistic regression based on homomorphic encryption.”
- FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “As demonstrated in Figure 3, FL has various topology definitions, such as vertical FL [34, 35, 36, 37, 38, 39, 40], split learning [41, 42], decentralized FL [43, 44, 45, 46], and Hierarchical FL [47, 48, 49, 50, 51, 52].”
- Self-supervised Cross-silo Federated Neural Architecture Search (2021, background): “[33] studied the VFL problem without the presence of third-party coordinator, and presented a solution for parallel distributed logistic regression.”
- Asymmetrical Vertical Federated Learning (2020, methodology): “…schemes that take the distributed output of APSI protocols and yield the same or almost the same result as the SVFL. Based on the standard vertical model training in [Yang et al. , 2019a], we now present a Genuine with Dummy (GWD) approach to achieving asymmetrical model training as follows.”
- A Comprehensive Survey of Privacy-preserving Federated Learning (2021, unknown): “[206] proposed a vertical FL framework for a logistic regress model without a third-party coordinator.”
- Vertical Federated Learning: Concepts, Advances, and Challenges (2022, background): “Follow-up works BaiduLR [109] and SecureLR [110] remove the coordinator from the training and inference procedure by relaxing either the efficiency or privacy constraint.”
- Federated Learning on Non-IID Data: A Survey (2021, unknown): “introduce a simplified two-party vertical FL framework [151] by removing the third party coordinator.”