成员证明的隐私保护联邦学习(译Privacy-preserving federated learning with membership proof) 隐私保护联邦学习是一种分布式机器学习,由多个协作者通过受保护的梯度训练模型。为了实现对用户退出的鲁棒性,现有的实用隐私保护联合学习方案都是基于(t,N)门限秘密共享的。但这样的方案通常需要依靠一个强有力的假设来保证安全...
鉴于组会汇报,特地挑了篇论文来简单学习一下,本文原文A Privacy-Preserving Federated Learning for Multiparty Data Sharing,Yin et al. IEEE Trans. 2021.随着5G和移动计算的快速发展,社交计算和社交物联网(I…
Training supervised machine learning models like deep learning requires high-quality labelled datasets that contain enough samples from various categories
Federated learning (FL) is an important approach to cooperate with multiple devices for learning without exchanging data between devices and central server. However, due to bandwidth and other reasons, the communication efficiency should be considered when the volume of information transmitted is limited...
To this end, in this paper, we reiterate the concept of federated learning and propose secure federated learning (SFL), where the ultimate goal is to build trustworthy and safe AI with strong privacy-preserving and IP-right-preserving. We provide a comprehensive overview of existing works, ...
In view of the above issues, we propose a highly efficient federated learning with strong privacy preservation in cloud computing (HFWP). By designing a new encryption protocol to perform SMC, we define a simple privacy-preserving FL approach, which also allows some clients to dropout during the...
A privacy-preserving federated learning protocol with a secure data aggregation for the Internet of Everything Although there are significant advantages to the popular use of connected devices on the Internet of Everything, there remain distinct concerns surrounding... S Basudan - 《Computer Communicat...
Gong, Xuan, et al. "Preserving privacy in federated learning with ensemble cross-domain knowledge distillation." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 11. 2022.——CCF-A 该文章作者提出了一种新颖的基于蒸馏的联邦学习框架,即 FedKD,它可以通过仅使用未...
个人学习 在Individual_Training_Client1.py,Individual_Training_Client2.py,Individual_Training_Client3.py中:(1)运行train_model(epochs)以运行模型(2)运行threshold_calculation(P 点赞(0)踩踩(0)反馈 所需:5积分电信网络下载 sap 代码管理工具 abapgit abap版本管理工具 ...
为了应对这一重要挑战,本文提出了一种称为异构联邦迁移学习(Heterogeneous Federated Transfer Learning,HFTL) 的新技术,以使联邦学习能够使用迁移学习处理异构特征空间。 我们设计了一种保护隐私的迁移学习方法,以消除同质特征空间的协变量偏移(Covariate Shift[1],一言以蔽之:样本点概率密度的变化),并桥接不同数据持有...