联邦元学习综述

Federated meta learning:a review

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。

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.