群体智能中的联邦学习算法综述

杨强; 童咏昕; 王晏晟; 范力欣; 王薇; 陈雷; 王魏; 康焱

doi:10.11959/j.issn.2096-6652.202218

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群体智能中的联邦学习算法综述

专题：群体智能 | 更新时间：2024-06-05

- 群体智能中的联邦学习算法综述
- A survey on federated learning in crowd intelligence
- 智能科学与技术学报 2022年4卷第1期页码：29-44
- 作者机构：
  
  1. 深圳前海微众银行股份有限公司，广东深圳 518063
  2. 香港科技大学，香港 999077
  3. 北京航空航天大学，北京 100191
  4. 南京大学，江苏南京 210033
- 作者简介：
  
  [ "杨强（1961− ），男，博士，深圳前海微众银行股份有限公司首席人工智能官，香港科技大学教授，主要研究方向为联邦学习、迁移学习、群体智能等" ]
  [ "童咏昕（1982− ），男，博士，北京航空航天大学教授，主要研究方向为联邦学习、群体智能、数据库与数据挖掘" ]
  [ "王晏晟（1994− ），男，北京航空航天大学博士生，主要研究方向为联邦学习" ]
  [ "范力欣（1971− ），男，博士，深圳前海微众银行股份有限公司人工智能首席科学家，主要研究方向为机器学习、联邦学习、计算机视觉" ]
  [ "王薇（1983− ），女，博士，北京航空航天大学教授，主要研究方向为群智系统协同控制与优化、攻击检测与安全控制" ]
  [ "陈雷（1972− ），男，博士，香港科技大学教授，主要研究方向为时空大数据、空间众包、不确定数据、数据驱动的机器学习" ]
  [ "王魏（1983− ），男，博士，南京大学副教授，主要研究方向为机器学习、弱监督学习、计算学习理论" ]
  [ "康焱（1984− ），男，博士，深圳前海微众银行股份有限公司人工智能算法研究员，主要研究方向为隐私保护机器学习、联邦迁移学习" ]
- 基金信息：
  
  国家重点研发计划资助项目;The National Key Research and Development Program of China(2018AAA0101100);国家自然科学基金资助项目;The National Natural Science Foundation of China(U21A20516);国家自然科学基金资助项目;The National Natural Science Foundation of China(61822201);国家自然科学基金项目;The National Natural Science Foundation of China(U1811463);国家自然科学基金资助项目;The National Natural Science Foundation of China(62076017);微众学者计划;WeBank Scholars Program
- DOI：10.11959/j.issn.2096-6652.202218
  中图分类号： TP39
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-15
- 稿件说明：
移动端阅览
杨强, 童咏昕, 王晏晟, 等. 群体智能中的联邦学习算法综述[J]. 智能科学与技术学报, 2022,4(1):29-44.

Qiang YANG, Yongxin TONG, Yansheng WANG, et al. A survey on federated learning in crowd intelligence[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 29-44.
杨强, 童咏昕, 王晏晟, 等. 群体智能中的联邦学习算法综述[J]. 智能科学与技术学报, 2022,4(1):29-44. DOI： 10.11959/j.issn.2096-6652.202218.

Qiang YANG, Yongxin TONG, Yansheng WANG, et al. A survey on federated learning in crowd intelligence[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 29-44. DOI： 10.11959/j.issn.2096-6652.202218.

摘要

群体智能是在互联网高速普及下诞生的人工智能新范式。然而，数据孤岛与数据隐私保护问题导致群体间数据共享困难，群体智能应用难以构建。联邦学习是一类新兴的打破数据孤岛、联合构建群智模型的重要方法。首先，介绍了联邦学习的基础概念以及其与群体智能的关系；其次，基于群体智能视角对联邦学习算法框架进行了分类，从隐私、精度与效率3个角度讨论了联邦学习算法优化技术；而后，阐述了基于线性模型、树模型与神经网络模型的联邦学习算法模型；最后，介绍了联邦学习代表性开源平台与典型应用，并对联邦学习研究进行总结展望。

Abstract

Crowd intelligence is emerging as a new artificial intelligence paradigm owing to the rapid development of the Internet.However

the data isolation and data privacy preservation problems make it difficult to share data among the crowd and to build crowd intelligent applications.Federated learning is a novel solution that aims to collaboratively build models by breaking the data barriers in crowd.Firstly

the basic ideas of federated learning and a comparison with crowd intelligence were introduced.Secondly

federated learning algorithms were divided into three categories according to the crowd organization

and further optimization techniques on privacy

accuracy and efficiency were discussed.Thirdly

federated learning operators based on linear models

tree models and neural network models were presented respectively.Finally

mainstream federated learningopensource platforms and typical applications were introduced

followed by the conclusion.

关键词

Keywords

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