联邦元学习综述

张传尧; 司世景; 王健宗; 肖京

doi:10.11959/j.issn.2096-0271.2022051

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联邦元学习综述

研究 | 更新时间：2024-06-03

- 联邦元学习综述
- Federated meta learning: a review
- 大数据 2023年9卷第2期页码：122-146
- 作者机构：
  
  1. 平安科技（深圳）有限公司，广东深圳 518063
  2. 中国科学技术大学，安徽合肥 230026
- 作者简介：
  
  [ "张传尧（1998- ）,男，中国科学技术大学硕士研究生，平安科技（深圳）有限公司算法工程师，主要研究方向为元学习和联邦学习" ]
  [ "司世景（1988- ），男，博士，平安科技（深圳）有限公司资深算法研究员，深圳市海外高层次人才，美国杜克大学人工智能博士后，中国计算机学会会员，主要研究方向为机器学习和及其在人工智能领域应用" ]
  [ "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，中国计算机学会高级会员，中国计算机学会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" ]
  [ "肖京（1972- ），男，博士，平安集团首席科学家，2019年吴文俊人工智能杰出贡献奖获得者，中国计算机学会深圳分部副主席，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：
  
  广东省重点领域研发计划“新一代人工智能”重大专项;The Key Research and Development Program of Guangdong Province(2021B0101400003)
- DOI：10.11959/j.issn.2096-0271.2022051
  中图分类号： TP181
- 网络首发：2023-03，
  
  纸质出版：2023-03-15
- 稿件说明：
移动端阅览
张传尧, 司世景, 王健宗, 等. 联邦元学习综述[J]. 大数据, 2023,9(2):122-146.

Chuanyao ZHANG, Shijing SI, Jianzong WANG, et al. Federated meta learning: a review[J]. Big data research, 2023, 9(2): 122-146.
张传尧, 司世景, 王健宗, 等. 联邦元学习综述[J]. 大数据, 2023,9(2):122-146. DOI： 10.11959/j.issn.2096-0271.2022051.

Chuanyao ZHANG, Shijing SI, Jianzong WANG, et al. Federated meta learning: a review[J]. Big data research, 2023, 9(2): 122-146. DOI： 10.11959/j.issn.2096-0271.2022051.

摘要

随着移动设备的普及，海量的数据在不断产生。数据隐私政策不断细化，数据的流动和使用受到严格监管。联邦学习可以打破数据壁垒，联合利用不同客户端数据进行建模。由于用户使用习惯不同，不同客户端数据之间存在很大差异。如何解决数据不平衡带来的统计挑战，是联邦学习研究的一个重要课题。利用元学习的快速学习能力，为不同数据节点训练不同的个性化模型来解决联邦学习中的数据不平衡问题成为一种重要方式。从联邦学习背景出发，系统介绍了联邦学习的问题定义、分类方式及联邦学习面临的主要问题。主要问题包括：隐私保护、数据异构、通信受限。从联邦元学习的背景出发，系统介绍了联邦元学习在解决联邦学习数据异构、通信受限问题及提高恶意攻击下鲁棒性方面的研究工作，对联邦元学习的工作进行了总结展望。

Abstract

With the popularity of mobile devices

massive amounts of data are constantly produced.The data privacy policies are becoming more and more specified

the flow and use of data are strictly regulated.Federated learning can break data barriers and use client data for modeling.Because users have different habits

there are significant differences between different client data.How to solve the statistical challenge caused by the data imbalance becomes an important topic in federated learning research.Using the fast learning ability of meta learning

it becomes an important way to train different personalized models for different clients to solve the problem of data imbalance in federated learning.The definition and classification of federated learning

as well as the main problems of federated learning were introduced systematically based on the background of federated learning.The main problems included privacy protection

data heterogeneity and limited communication.The research work of federated metalearning in solving the heterogeneous data

the limited communication environment

and improving the robustness against malicious attacks were introduced systematically starting from the background of federated meta learning.Finally

the summary and prospect of federated meta learning were proposed.

关键词

Keywords

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