Preventing harm to the rare in combating the malicious: A filtering-and-voting framework with adaptive aggregation in federated learning-Reference-Cited by-同舟云学术

登录注册会员服务联系我们

Preventing harm to the rare in combating the malicious: A filtering-and-voting framework with adaptive aggregation in federated learning

Published:2024-11 Issue: Volume:604 Page:128317
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Jiang Yanna^ORCID,Ma Baihe^ORCID,Wang Xu^ORCID,Yu Guangsheng^ORCID,Sun Caijun^ORCID,Ni Wei^ORCID,Liu Ren Ping^ORCID

Publisher

Elsevier BV

Reference51 articles.

1. Federated learning: Challenges, methods, and future directions;Li;IEEE Signal Process. Mag.,2020

2. Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions;Yaacoub;Internet Things Cyber-Phys. Syst.,2023

3. Blockchained federated learning for internet of things: A comprehensive survey;Jiang;ACM Comput. Surv.,2024

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

5. Machine learning with adversaries: Byzantine tolerant gradient descent;Blanchard;Adv. Neural Inf. Process. Syst.,2017

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370

www.globalauthorid.com

TOP