Results of Fed2DP

Name: Results of Fed2DP
Creator: Chen Gu
Published: 2026-04-09T12:25:34+08:00

Mendeley Data2026-04-09 收录

联邦学习隐私保护

差分隐私

数据链接：

https://data.mendeley.com/datasets/zzvh654fy7/1 数据链接链接失效反馈

官方服务：

资源简介：

The dataset contains results in the paper "Fed2DP: Privacy-Preserving Federated Learning via Differential Privacy".

提供机构：

Chen Gu

Formal definition of threat models.

联邦学习隐私保护

动态权衡优化

Federated learning faces a fundamental privacy-utility-communication trilemma, and existing static defense mechanisms suffer from rigid adaptation and poor multidimensional coordination, leaving a cri

NIAID Data Ecosystem80

Employee Dataset (EMP-DS) data model.

数据隐私保护

差分隐私

Organizations increasingly integrate and share person-level data across internal platforms and external partners to enable analytics, digital services, and evidence-based decision making. However, com

Figshare2026-03-30 更新40

"Differentially Private Zeroth-Order Methods for Scalable Large Language Model Fine-tuning"

差分隐私

语言模型微调

"This is the implementation for the paper Differentially Private Zeroth-Order Methods for Scalable Large Language Model Fine-tuning. In this paper, we investigate the potential of DP zeroth-order meth

DataCite Commons2025-08-12 更新40

Reproducible Privacy: Using Differential Privacy to Share Sensitive Research Data. Supplementary Materials.

差分隐私

敏感数据共享

Reproduction Material for: Reproducible Privacy: Using Differential Privacy to Share Sensitive Research Data

DataONE2020-08-20 更新50

A robust privacy-preserving online monitoring algorithm for disease surveillance

差分隐私

疾病早期预警

Early detection and treatment of disease are paramount for our well-being. Regular clinical visits are essential for patients with chronic illnesses to prevent the disease from progressing, and indivi

Figshare2025-03-10 更新40

Results of Fed2DP

资源简介：

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