In this paper, we propose a Privacy-Enhanced Momentum Federated Learning framework, named PEMFL, that amalgamates differential privacy (DP), Momentum FL (MFL) and chaos-based encryption method. During the training, differentially privacy is used to disturb the industrial agents' gradient parameters ...
Privacy-Enhanced Federated Learning against Poisoning Adversaries 原文链接: 今天分享的是发表在 TIFS2021 上的一篇论文,主要关注是的隐私保护的联邦学习问题(preserving-privacy federated learning , PPFL)。单纯的 PPFL 方案致力于各方的模型信息不可区分来抵御推理攻击,而抗投毒攻击的方案则致力于根据异常数据与正常...
PrivacyEnhanced Federated Learning against Poisoning Adversaries 是一种结合了同态加密和中位数技术的联邦学习框架，旨在同时保护模型隐私和对抗投毒攻击。以下是关于PEFL的详细解答：目的与背景：PEFL旨在解决传统隐私保护的联邦学习与抗投毒方案之间的目标冲突。PPFL主要关注保护模型信息的不可区分性以对抗推理...
Similarly, a privacy-enhanced federated learning scheme against an untrusted server was proposed in Zhang et al. (2019), which used the Paillier homomorphic cryptosystem to aggregate the local gradient of a group of participants, thereby protecting individuals’ private information. Moreover, Mandal ...
To this end, we propose a privacy-enhanced federated learning data fusion strategy. This strategy not only adds differential privacy noise in the local model training process but also adds differential privacy noise in the federated training process, so as to realize the differential privacy ...
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...
With the increasing awareness of data privacy protection and the growing stringency of data security regulations, federated learning (FL) as a distributed machine learning approach has garnered widespread attention. However, in practice, FL faces severe challenges in privacy protection. This paper propos...
Hao M, Li H, Luo X, Xu G, Yang H, Liu S (2019) Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans Indust Inform Hard A, Rao K, Mathews R, Beaufays F, Augenstein S, Eichner H, Kiddon C, Ramage D (2018) Federated learning for mobile...
Abstract— In federated learning for VANET, Yuan et al. proposed a privacy-enhanced authentication protocol to balance training efficiency and vehicle privacy. They claimed that their protocol provides both unforgeability and traceability, asserting that it is secure against unforgeable attacks from Typ...
In this course, Federated Learning and Privacy-preserving RAGs, you’ll learn to design and implement advanced AI systems that prioritize data privacy without sacrificing performance. First, you’ll explore the fundamentals of federated learning, including its principles and how it enables decentralized...