ESE: Efficient Security Enhancement Method for the Secure Aggregation Protocol in Federated Learning
Author:
Affiliation:
1. School of Computer Science and Engineering, Sun Yat-Sen University,Guangzhou,China,510275
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/9970761/10124650/10124688.pdf?arnumber=10124688
Reference45 articles.
1. Secure linear aggregation using decentralized threshold additive homomorphic encryption for federated learning;tian;ArXiv Preprint,2021
2. Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning
3. SwiftAgg: Communication-Efficient and Dropout-Resistant Secure Aggregation for Federated Learning with Worst-Case Security Guarantees
4. Privacy-preserving distributed clustering using generative models
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. Federated Learning for Privacy-Preserving Intrusion Detection in Software-Defined Networks;IEEE Access;2024
2. Post-quantum Dropout-Resilient Aggregation for Federated Learning via Lattice-Based PRF;Lecture Notes in Computer Science;2024
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