Federated learning for teacher data privacy protection: a study in the context of the PIPL

FL conceptually supports teacher data privacy protection under the PIPL framework. This study proposes a tailored compliance framework that integrates FL with privacy-enhancing technologies, offering theoretical foundations and practical recommendations for educational institutions and technology implementers to deploy privacy-preserving machine learning solutions.

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Publication Details

Journal
Frontiers in Big Data
Published
2026-02-09
DOI
https://doi.org/10.3389/fdata.2026.1681382
Citations
1
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
Field-Weighted Citation Impact
52.10

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article

Federated learning for teacher data privacy protection: a study in the context of the PIPL

Xiu Zhi Qi, Shanwei Chen, Xue Hui Han, Zhao Chen Fan et al.
1 citations
Frontiers in Big Data
Privacy-Preserving Technologies in Data
52.10
article

Federated learning for teacher data privacy protection: a study in the context of the PIPL

Xiu Zhi Qi, Shanwei Chen, Xue Hui Han, Zhao Chen Fan, Le Le Wang
article en
1 citations

Abstract

FL conceptually supports teacher data privacy protection under the PIPL framework. This study proposes a tailored compliance framework that integrates FL with privacy-enhancing technologies, offering theoretical foundations and practical recommendations for educational institutions and technology implementers to deploy privacy-preserving machine learning solutions.

Frontiers in Big DataVol. 9
Baoji University of Arts and Sciences (CN)
Ministry of Education of the People's Republic of China, Baoji University of Arts And Sciences, Ministry of Education, India, Humanities and Social Science Fund of Ministry of Education of China
Peace, Justice and strong institutions
Openalex Percentile: Top 1%
Privacy-Preserving Technologies in Data
52.10
DOIOpen Access PDFOpenAlex
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