1.A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection 作者:Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He 论文概要:在本次调查中,我们对联邦学习系统进行了全面审查。为了理解关键设计系统组件并指导未来的研究,我...
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectiondoi:10.1109/TKDE.2021.3124599Qinbin LiZeyi WenZhaomin WuSixu HuNaibo WangYuan LiXu LiuBingsheng HeIEEE, Institute of Electrical and Electronics Engineers...
A survey on federated learning Authors Chen Zhang, Yu Xie, Hang Bai, Bin Yu, Weihong Li, Yuan Gao Keywords Federated learning; Privacy protection; Mac
A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond Authors Sawsan AbdulRahman, Hanine Tout, Hakim
A survey on federated learning Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggre... C Zhang,Y Xie,H Bai,... - Knowledge-Based Systems 被引量: 0发表: 2021年 Strategies for Facilitating...
结束那篇巨长的综述后,又开了篇综述坑(悲),但是这次标题顾名思义,是联邦学习安全和隐私方面的综述文章,A Survey on Security and Privacy of Federated Learning,于2020年发表于Future Generation Computer Systems期刊上,文章相比之前那篇121页的综述短很多(但还是有61页啊啊啊啊啊啊),内容也更集中些。 不多说...
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. arxiv 2021 paper bib Qinbin Li, Zeyi Wen, Zhaomin Wu, Sixu Hu, Naibo Wang, Yuan Li, Xu Liu, Bingsheng He A Survey towards Federated Semi-supervised Learning. arXiv 2020 paper bib Yilun Jin...
SFFAI论坛网站已开放注册,详情点击查看:https://bbs.sffai.com/d/312 关注公众号:【人工智能前沿讲习】,回复【SFFAI142】获取讲者PPT资料,入交流群,推荐论文下载。 分享亮点: 1. 我们是第一个提出并定义联邦类别增量学习的工作,该问题的主要挑战是不损害联邦学习隐私保护功能的前提下,有效地缓解联邦学习在旧类别数...
A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction Researcher: Georgios Damaskinos, MLSys, https://people.epfl.ch/georgios.damaskinos?lang=en Heterogeneity-Aware Federated Lea...
联邦学习(Federated Learning)在大数据时代逐渐兴起,主要是为了解决数据隐私问题。随着数据隐私的重要性不断提升,人们越来越不愿意将自己的敏感信息交给中央服务器处理。联邦学习通过在用户设备本地训练模型,并将每个客户端的模型参数而非原始数据进行汇总,旨在保护用户隐私。 尽管联邦学习具有一定的隐私保护能力,但其分布式...