Asymmetrical Vertical Federated Learning

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.

References in corpus (2)

• Communication-Efficient Learning of Deep Networks from Decentralized Data
• Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinator

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• FedML: A Research Library and Benchmark for Federated Machine Learning
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• Practical Vertical Federated Learning with Unsupervised Representation Learning
• Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
• Vertical Federated Learning without Revealing Intersection Membership
• Self-supervised Cross-silo Federated Neural Architecture Search
• Residue-based Label Protection Mechanisms in Vertical Logistic Regression
• Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges
• From Federated Learning to Federated Neural Architecture Search: A Survey
• Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications
• Management of Resource at the Network Edge for Federated Learning
• BadVFL: Backdoor Attacks in Vertical Federated Learning