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EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party

Authors:

Ming YaoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 3

Article No.: 51, Pages 1 - 20

https://doi.org/10.1145/3627993

Published: 09 December 2023 Publication History

Abstract

Federated learning (FL) is a machine learning setting which allows multiple participants collaboratively to train a model under the orchestration of a server without disclosing their local data. Vertical federated learning (VFL) is a special structure in FL. It handles the situation where participants have the same ID space but different feature spaces. In order to guarantee the security and privacy of the local data of each participant, homomorphic encryption (HE) is often used to transmit intermediate parameters or data during the training process. In most VFL frameworks, a trusted third-party server is necessary because the plaintexts of the parameters need to be revealed for the computation. However, it is hard to find such a credible entity in the real world. Existing methods for solving this problem are either communication-intensive or unsuitable for multi-party scenarios. By combining secret sharing (SS) and HE, we propose a novel VFL framework without any trusted third parties called EFMVFL. It allows intermediate parameters to be transmitted among multiple parties without revealing the plaintexts. EFMVFL is applicable to generalized linear models (GLMs) and supports flexible expansion to multiple participants. Extensive experiments under Logistic Regression and Poisson Regression show that our framework is outstanding in communication (reduced by 3.2×– 6.8×) and efficiency (accelerated by 1.6×– 3.1×).

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Cited By

Fan MZhang ZLi ZSun GYu HKang JGuizani M(2024)SecureVFL: privacy-preserving multi-party vertical federated learning based on blockchain and RSSDigital Communications and Networks10.1016/j.dcan.2024.07.008Online publication date: Aug-2024
https://doi.org/10.1016/j.dcan.2024.07.008

Index Terms

EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party
1. Computing methodologies
  1. Machine learning
2. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 3

April 2024

663 pages

EISSN:1556-472X

DOI:10.1145/3613567

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 December 2023

Online AM: 17 October 2023

Accepted: 12 October 2023

Revised: 14 August 2023

Received: 12 September 2022

Published in TKDD Volume 18, Issue 3

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Cited By

Fan MZhang ZLi ZSun GYu HKang JGuizani M(2024)SecureVFL: privacy-preserving multi-party vertical federated learning based on blockchain and RSSDigital Communications and Networks10.1016/j.dcan.2024.07.008Online publication date: Aug-2024
https://doi.org/10.1016/j.dcan.2024.07.008

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