Asymmetrical Vertical Federated Learning

2020/04/16 by Yang Liu, Xiong Zhang, Liu, Yang +3 · 3 citations
Computer Science · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Machine Learning (cs.LG) #Privacy-Preserving Technologies in Data #cs.CR #cs.LG

paper · pdf · doi:10.48550/arxiv.2004.07427

openalex publication_date 2020/04/16 · arxiv created 2020/06/11 · arxiv updated 2020/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Federated learning is a distributed machine learning method that aims to preserve the privacy of sample features and labels. In a federated learning system, ID-based sample alignment approaches are usually applied with few efforts made on the protection of ID privacy. In real-life applications, however, the confidentiality of sample IDs, which are the strongest row identifiers, is also drawing much attention from many participants. To relax their privacy concerns about ID privacy, this paper formally proposes the notion of asymmetrical vertical federated learning and illustrates the way to protect sample IDs. The standard private set intersection protocol is adapted to achieve the asymmetrical ID alignment phase in an asymmetrical vertical federated learning system. Correspondingly, a Pohlig-Hellman realization of the adapted protocol is provided. This paper also presents a genuine with dummy approach to achieving asymmetrical federated model training. To illustrate its application, a federated logistic regression algorithm is provided as an example. Experiments are also made for validating the feasibility of this approach.

Citations

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

Cited by

• Applications of federated learning in smart cities: recent advances, taxonomy, and open challenges
• PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
• Management of Resource at the Network Edge for Federated Learning
• Self-supervised Cross-silo Federated Neural Architecture Search

Related

• Vertical Federated Learning: A Structured Literature Review
• FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
• Privacy-Preserving Self-Taught Federated Learning for Heterogeneous Data
• Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learning
• Vertical Federated Learning without Revealing Intersection Membership