Open Access

ARTICLE

Vertical Federated Learning Based on Consortium Blockchain for Data Sharing in Mobile Edge Computing

Yonghao Zhang^1,3, Yongtang Wu², Tao Li¹, Hui Zhou^1,3, Yuling Chen^1,2,*

1 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China
2 Blockchain Laboratory of Agricultural Vegetables, Weifang University of Science and Technology, Weifang, 262700, China
3 Guangxi Key Laboratory of Cryptography and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

* Corresponding Author: Yuling Chen. Email:

(This article belongs to the Special Issue: Artificial Intelligence for Mobile Edge Computing in IoT)

Computer Modeling in Engineering & Sciences 2023, 137(1), 345-361. https://doi.org/10.32604/cmes.2023.026920

Received 03 October 2022; Accepted 22 December 2022; Issue published 23 April 2023

Abstract

The data in Mobile Edge Computing (MEC) contains tremendous market value, and data sharing can maximize the usefulness of the data. However, certain data is quite sensitive, and sharing it directly may violate privacy. Vertical Federated Learning (VFL) is a secure distributed machine learning framework that completes joint model training by passing encrypted model parameters rather than raw data, so there is no data privacy leakage during the training process. Therefore, the VFL can build a bridge between data demander and owner to realize data sharing while protecting data privacy. Typically, the VFL requires a third party for key distribution and decryption of training results. In this article, we employ the consortium blockchain instead of the traditional third party and design a VFL architecture based on the consortium blockchain for data sharing in MEC. More specifically, we propose a V-Raft consensus algorithm based on Verifiable Random Functions (VRFs), which is a variant of the Raft. The VRaft is able to elect leader quickly and stably to assist data demander and owner to complete data sharing by VFL. Moreover, we apply secret sharing to distribute the private key to avoid the situation where the training result cannot be decrypted if the leader crashes. Finally, we analyzed the performance of the V-Raft and carried out simulation experiments, and the results show that compared with Raft, the V-Raft has higher efficiency and better scalability.

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Keywords

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