联邦学习算法综述

王健宗; 孔令炜; 黄章成; 陈霖捷; 刘懿; 何安珣; 肖京

doi:10.11959/j.issn.2096-0271.2020055

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联邦学习算法综述

研究 | 更新时间：2024-06-03

- 联邦学习算法综述
- Research review of federated learning algorithms
- 大数据 2020年6卷第6期页码：2020055-1
- 作者机构：
  
  1. 平安科技（深圳）有限公司，广东深圳 518063
  2. 中国平安保险（集团）股份有限公司，广东深圳 518031
- 作者简介：
  
  [ "王健宗（1983- ），男，博士，平安科技（深圳）有限公司副总工程师，资深人工智能总监，联邦学习技术部总经理。美国佛罗里达大学人工智能博士后，中国计算机学会（CCF）高级会员，CCF大数据专家委员会委员，曾任美国莱斯大学电子与计算机工程系研究员，主要研究方向为联邦学习和人工智能等" ]
  [ "孔令炜（1995- ），男，平安科技（深圳）有限公司联邦学习团队算法工程师，CCF会员，主要研究方向为联邦学习系统和安全通信等" ]
  [ "黄章成（1990- ），男，平安科技（深圳）有限公司联邦学习团队资深算法工程师，人工智能专家，CCF会员，主要研究方向为联邦学习、分布式计算及系统和加密通信等" ]
  [ "陈霖捷（1994- ），男，平安科技（深圳）有限公司联邦学习团队算法工程师，主要研究方向为联邦学习与隐私保护、机器翻译等" ]
  [ "刘懿（1994- ），女，平安科技（深圳）有限公司联邦学习团队算法工程师，主要研究方向为联邦学习系统等" ]
  [ "何安珣（1990- ），女，平安科技（深圳）有限公司联邦学习团队高级算法工程师，CCF会员，主要研究方向为联邦学习技术在金融领域的落地应用、联邦学习框架搭建、加密算法研究和模型融合技术" ]
  [ "肖京（1972- ），男，博士，中国平安保险（集团）股份有限公司首席科学家。2019年吴文俊人工智能科学技术奖杰出贡献奖获得者，CCF深圳会员活动中心副主席，主要研究方向为计算机图形学学科、自动驾驶、3D显示、医疗诊断、联邦学习等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2018YFB1003503);国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2018YFB0204400);国家重点研发计划基金资助项目;The National Key Research and Development Program of China(2017YFB1401202)
- DOI：10.11959/j.issn.2096-0271.2020055
  中图分类号： TP311
- 网络首发：2020-11，
  
  纸质出版：2020-11-15
- 稿件说明：
移动端阅览
王健宗, 孔令炜, 黄章成, 等. 联邦学习算法综述[J]. 大数据, 2020,6(6):2020055-1.

Jianzong WANG, Lingwei KONG, Zhangcheng HUANG, et al. Research review of federated learning algorithms[J]. Big Data Research, 2020, 6(6): 2020055-1.
王健宗, 孔令炜, 黄章成, 等. 联邦学习算法综述[J]. 大数据, 2020,6(6):2020055-1. DOI： 10.11959/j.issn.2096-0271.2020055.

Jianzong WANG, Lingwei KONG, Zhangcheng HUANG, et al. Research review of federated learning algorithms[J]. Big Data Research, 2020, 6(6): 2020055-1. DOI： 10.11959/j.issn.2096-0271.2020055.

摘要

近年来，联邦学习作为解决数据孤岛问题的技术被广泛关注，已经开始被应用于金融、医疗健康以及智慧城市等领域。从3个层面系统阐述联邦学习算法。首先通过联邦学习的定义、架构、分类以及与传统分布式学习的对比来阐述联邦学习的概念；然后基于机器学习和深度学习对目前各类联邦学习算法进行分类比较和深入分析；最后分别从通信成本、客户端选择、聚合方式优化的角度对联邦学习优化算法进行分类，总结了联邦学习的研究现状，并提出了联邦学习面临的通信、系统异构、数据异构三大难题和解决方案，以及对未来的期望。

Abstract

In recent years

federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field

healthcare domain and smart city related application.Federated learning concept was introduced into three different layers.The first layer introduced the definition

architecture

classification of federated learning and compared the federated learning with traditional distributed learning.The second layer presented comparison and analysis of federated learning algorithms from machine learning and deep learning aspects.The third layer separated federated learning optimization algorithms into three aspects to optimize federated learning algorithm through reducing communication cost

selecting proper clients and different aggregation method.Finally

the current research status and three main challenges on communication

heterogeneity of system and data to be solved were concluded

and the future prospects in federated learning domain were proposed.

关键词

Keywords

references

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