HQsFL: A Novel Training Strategy for Constructing High-performance and Quantum-safe Federated Learning-Reference-Cited by-同舟云学术

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HQsFL: A Novel Training Strategy for Constructing High-performance and Quantum-safe Federated Learning

Published:2024-07 Issue: Volume: Page:512-521
ISSN:
Container-title:Proceedings of the 19th ACM Asia Conference on Computer and Communications Security
language:
Short-container-title:

Author:

Yu Bo¹^ORCID,Shen Huajie¹^ORCID,Xu Qian¹^ORCID,He Wei¹^ORCID,Mao Wankui¹^ORCID,Zhang Qing¹^ORCID,Zhang Fan²^ORCID

Affiliation:

1. Blockchain Research Institute, Bestpay Co., Ltd., Shanghai, China

2. School of Cyber Science and Technology, College of Computer Science and Technology, Zhejiang University, Hangzhou, China

Funder

Ministry of Science and Technology of the People's Republic of China

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

the Fok Ying-Tung Education Foundation for Young Teachers in the Higher Education Institutions of China

Publisher

ACM

Reference42 articles.

1. On the concrete hardness of Learning with Errors

2. Privacy-Preserving Logistic Regression with Distributed Data Sources via Homomorphic Encryption

3. Ayoub Benaissa Bilal Retiat Bogdan Cebere and Alaa Eddine Belfedhal. 2021. TenSEAL: A Library for Encrypted Tensor Operations Using Homomorphic Encryption. arXiv:2104.03152 [cs.CR]

4. Daniel J Bernstein and Tanja Lange. 2017. Post-quantum cryptography. Nature 549, 7671 (2017), 188--194.

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