Blockchain-based privacy-preserving multi-tasks federated learning framework-Reference-Cited by-同舟云学术

Blockchain-based privacy-preserving multi-tasks federated learning framework

Published:2024-01-08 Issue:1 Volume:36 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Jia Yunyan¹^ORCID,Xiong Ling¹^ORCID,Fan Yu²^ORCID,Liang Wei³^ORCID,Xiong Neal⁴^ORCID,Xiao Fengjun⁵^ORCID

Affiliation:

1. School of Computer and Software Engineering, Xihua University, Chengdu, People's Republic of China

2. Xihua Honor College, Xihua University, Chengdu, People's Republic of China

3. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People's Republic of China

4. Department of Computer Science and Mathematics, Sul Ross State University, Alpine, TX, USA

5. Zhejiang Informatization Development Institute, Hangzhou Dianzi University, Hangzhou, People's Republic of China

Funder

National Natural Science Foundation of China

Science and Technology Fund of Sichuan Province

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2023.2299103

Reference55 articles.

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3. FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive

4. Bonawitz K. Ivanov V. Kreuter B. Marcedone A. Mcmahan H. B. Patel S. Ramage D. Segal A. & Seth K. (2017). Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications securit (pp. 1175–1191).

5. Building a Secure Knowledge Marketplace Over Crowdsensed Data Streams

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