ESVFL: Efficient and secure verifiable federated learning with privacy-preserving-Reference-Cited by-同舟云学术

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ESVFL: Efficient and secure verifiable federated learning with privacy-preserving

Published:2024-09 Issue: Volume:109 Page:102420
ISSN:1566-2535
Container-title:Information Fusion
language:en
Short-container-title:Information Fusion

Author:

Cai Jiewang,Shen Wenting^ORCID,Qin Jing

Funder

Shandong Province Natural Science Foundation

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference57 articles.

1. Towards federated learning at scale: System design;Bonawitz;Proc. Mach. Learn. Syst.,2019

2. Federated learning: Strategies for improving communication efficiency;Konečnỳ,2016

3. Federated analytics: Opportunities and challenges;Wang;IEEE Netw.,2021

4. Advances and open problems in federated learning;Kairouz;Found. Trends® Mach. Learn.,2021

5. Practical secure aggregation for federated learning on user-held data;Bonawitz,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. More Efficient and Verifiable Privacy-Preserving Aggregation Scheme for Internet of Things-Based Federated Learning;Applied Sciences;2024-06-21

2. A Multifaceted Survey on Federated Learning: Fundamentals, Paradigm Shifts, Practical Issues, Recent Developments, Partnerships, Trade-Offs, Trustworthiness, and Ways Forward;IEEE Access;2024

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