Federated visualization:a new model for privacy-preserving visualization

Yating WEI; Zhiyong WANG; Shuyue ZHOU; Wei CHEN

doi:10.11959/j.issn.2096-6652.201946

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Federated visualization:a new model for privacy-preserving visualization

Regular Papers | 更新时间：2024-06-05

- Federated visualization:a new model for privacy-preserving visualization
- Chinese Journal of Intelligent Science and Technology Vol. 1, Issue 4, Pages: 415-420(2019)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.201946
  CLC： TP183
- Published Online：2019-12，
  
  Published：20 December 2019
- 稿件说明：
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Yating WEI, Zhiyong WANG, Shuyue ZHOU, et al. Federated visualization:a new model for privacy-preserving visualization[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 415-420.
DOI：

Yating WEI, Zhiyong WANG, Shuyue ZHOU, et al. Federated visualization:a new model for privacy-preserving visualization[J]. Chinese journal of intelligent science and technology, 2019, 1(4): 415-420. DOI： 10.11959/j.issn.2096-6652.201946.

摘要

概述了联邦可视化的概念、框架、方法与应用。联邦可视化框架能够在不进行数据整合的情况下，针对具体任务和特定场景进行加密训练，得出反映全体数据特征的可视化模型。联邦可视化是联邦学习框架在可视化领域的拓展应用，主要强调在保障数据隐私的前提下，互利共赢的联邦协作方式在对多数据源数据进行可视分析方面的应用，以打破各领域、各行业的数据壁垒，实现数据与知识的共享。

Abstract

The concept

architecture

methods and applications of federated visualization were introduced.The federated visualization framework is capable of encrypting and training a visual model that reflect the characteristics of the entire data for specific tasks and scenarios.The federated visualization framework is an extension and application of federated learning

which emphasized using mutual benefit and win-win federal cooperation to visually analyze multi-source data under the premise of ensuring data privacy

towards breaking down data barriers in various fields and industries and realizing the sharing of data and knowledge.

关键词

Keywords

references

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