这两个概念都具有the finest resolution,因为它们可以保护一个客户数据的单个记录。我们依赖的隐私定义是潜在隐私f-differential privacy,是其高斯潜在隐私 (GDP) 的子家族 (Dong等人,2019)。 2.我们提出了一个通用的联合学习框架PriFedSync,其中包含最先进的联合学习算法。框架不假定可信的中央聚合器。它可以容纳个...
Differential Privacy (DP), as an advanced privacy protection technology, introduces random noise during data queries or model updates, further enhancing the privacy protection capability of Federated Learning. This paper delves into the theory, technology, development, and futur...
differential privacy 2006年提出的，已经研究了十多年，纯DP的研究可以做的东西并不多了，学术界对DP的...
论文标题Dynamic Personalized Federated Learning with Adaptive Differential Privacy 论文作者 Xiyuan Yang, Wenke Huang, Mang Ye 科研机构 Wuhan University 发表会议 NeurIPS 2023 摘要概括 个性化联邦学习场景下的差分隐私有效解决数据非独立同分布和隐私泄露问题。然而,现有的个性化联邦学习场景下的差分隐私面临着两大挑...
In this experiment, we use Differential Private Federated Learning (DP-FL) to ensure data privacy. Differential Privacy (DP) was not considered in experiment series 1 since the objective was to study the effects of data size, distribution, and the number of clients on the performance of distrib...
Federated Learning with Differential Privacy:Algorithms and Performance Analysis 2024/2/11 大四做毕设的时候第一次读这篇论文,当时只读了前一部分,后面关于收敛界推导证明的部分没有看,现在重新完整阅读一下这篇文章。 本文贡献 提出了一种基于差分隐私 (DP) 概念的新框架,其中在聚合之前将人工噪声添加到客户端的...
This article proposes a privacy-preserving approach for learning effective personalized models on distributed user data while guaranteeing the differential privacy of user data. Practical issues in a distributed learning system such as user heterogeneity are considered in the proposed approach. In addition...
The key idea of FedDP is to deploy differential privacy on intermediate gradients that are computed and transmitted by optimizers from local parties. In addition, the unique weighted min-max loss in FedDP is deployed to address the challenge of fair prediction on highly imbalanced datasets. Our ...
In this report, we showcase our empirical benchmark of the effect of the number of clients and the addition of differential privacy (DP) mechanisms on the performance of the model on different types of data. Our results show that non-i.i.d and small datasets have the highest decrease in...
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