Privacy-Preserved Credit Data Sharing Integrating Blockchain and Federated Learning for Industrial 4.0

Fan Yang, Yanan Qiao, Mohammad Abedin, Cheng Huang

Research output: Contribution to journalArticlepeer-review


This paper aims to design an architecture for privacy-preserved credit data and model sharing to guarantee the secure storage and sharing of credit information in a distributed environment. The proposed architecture optimizes the data privacy by sharing the data model instead of revealing the actual data. This study also proposes an efficient credit data storage mechanism combined with deletable bloom filter to guarantee a uniform consensus for the training and computation process. In addition, we propose Authority Control Contract and Credit Verification Contract for secure certification of credit sharing model results under federated learning. Extensive experimental results and security analysis demonstrate that our proposed credit model sharing system based on Federated Learning and Blockchain is of high accuracy, efficiency, as well as stability. In particular, the findings of this paper could alleviate the potential credit crisis under financial pressure that assist to economic recovery after the global COVID-19 pandemic. Our approach has further boostsed up the demand for efficient, secure credit models for Industry 4.0.
Original languageEnglish
JournalIEEE Transactions on Industrial Informatics
Publication statusPublished - 16 Feb 2022

Bibliographical note

The National key RD Program of China (Grant Number: 2018YFB140270)


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