SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments-Reference-Cited by-同舟云学术

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SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments

Published:2024-04 Issue: Volume:239 Page:122410
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Cao Yihao^ORCID,Zhang Jianbiao^ORCID,Zhao Yaru^ORCID,Su Pengchong,Huang Haoxiang

Funder

People's Public Security University of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference42 articles.

1. Federated learning with personalization layers;Arivazhagan,2019

2. How to backdoor federated learning;Bagdasaryan,2020

3. Machine learning with adversaries: Byzantine tolerant gradient descent;Blanchard,2017

4. Fltrust: Byzantine-robust federated learning via trust bootstrapping;Cao,2020

5. EMNIST: Extending MNIST to handwritten letters;Cohen,2017

Cited by 7 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. A survey on membership inference attacks and defenses in machine learning;Journal of Information and Intelligence;2024-09

2. Emerging Authentication Technologies for Zero Trust on the Internet of Things;Symmetry;2024-08-05

3. Exploring Federated Learning Tendencies Using a Semantic Keyword Clustering Approach;Information;2024-06-28

4. Survey: federated learning data security and privacy-preserving in edge-Internet of Things;Artificial Intelligence Review;2024-04-29

5. FlexibleFL: Mitigating poisoning attacks with contributions in cloud-edge federated learning systems;Information Sciences;2024-04

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