联邦元学习综述

doi:10.11959/j.issn.2096-0271.2022051

Abstract

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.

Key words: federated learning, meta learning, heterogeneous data, federated meta learning, privacy protection

CLC Number:

TP181

Chuanyao ZHANG, Shijing SI, Jianzong WANG, Jing XIAO. Federated meta learning: a review[J]. Big Data Research, 2023, 9(2): 122-146.

Figures/Tables 10

分类	方法	特点
面向数据异构	FedMeta	使用一个共享的元学习者取代联邦学习中共享的全局模型，以更灵活的方式共享参数化算法
	Fedavg-Reptile	在联邦平均算法阶段后插入一个元学习微调阶段以提供一个可靠的初始化模型
	PerFedAvg	聚焦MAML算法在联邦学习情景下的收敛性分析，并提供了一个可证明收敛的方法解决函数为非凸的情况
	q-MAML	通过最小化一个聚合的加权损失函数，使得损失函数值较高的设备在目标优化函数中具有较高的相对权重从而促进模型在各个设备间的表现更加公平
	ARUBA	将在线凸优化和序列预测算法相结合，将元学习视为在线学习一系列损失
	PFL	在每轮训练过程中动态地修改设备损失函数以消除元模型对不同用户的偏见
	FedFomo	不再只学习一个单一的平均模型，通过n个服务器元模型，向不同客户端组发送不同的元模型
	FedRECON	利用元学习训练一个可以快速重构局部参数的全局参数，并在客户端上使用全局参数进行局部参数重构
面向资源挑战	NUFM	使用非均匀的设备选择方案NUFM促进模型收敛速度提高
	ADMM-FedMeta	将原始问题分解为许多子问题，可以跨边缘节点和平台并行解决，通过线性近似和海森估计将计算复杂度降为O(n)
	EEFML	该模型使用投影随机梯度上升（P-SGA）来反向查找元模型，大大降低了计算成本、提高了通信效率
	FedMeta w/UGA	提出了一种无偏梯度聚集算法（UGA）并引入一个元更新过程，通过一个可控的元训练集来促进优化目标向目标分布靠近
面向隐私保护	RobustFedML	通过构造对抗数据的方式实现算法的鲁棒性
	FL-MN	使用匹配网络的分类机制有效降低后门攻击的成功率，但是也会导致在一些任务上准确度降低
	Meta-FL	将防御检查点从用户端级别移动到聚合级别，有效缓解后门攻击带来的影响

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比较项	联邦学习	传统机器学习
数据隐私	强调数据隐私	无数据保护要求
通信环境	通信受限	通信连接良好
数据分布	分布式存储	数据集中

分类	联邦学习算法	特点
基于服务器端聚合方法优化的算法	FedSGD	每次使用客户端所有数据进行一轮梯度下降，收敛速度慢
基于服务器端聚合方法优化的算法	FedAvg	以客户端数据量与总数据量的比值作为权重，聚合不同客户端发送的参数，客户端使用多轮更新的方式加快模型收敛速度
	FedAvgM	通过引入动量更新缓解数据异构对联邦平均算法的影响
	FEDADAGRED	借鉴非联邦环境下的自适应优化器(ADAGRAD、ADAM和YOGI)提出了联邦学习版本
	FEDYOGI	的自适应优化器，通过自适应优化器显著提高了模型在数据异构情况下的收敛速度
	FEDADAM
	FedPER	通过共享基础层加个性化层的个性化模型提高模型公平性
	FedMA	以分层方式构建共享全局模型
基于客户端优化的算法	FedProx	客户端局部损失函数使用基于前一步权重的二次惩罚进行正则化，减少数据异构对模型的影响
	Fed-EMD	通过创建一个在所有数据节点间共享的数据子集，来减少数据不平衡的影响。需要很高的通信成本，降低隐私安全性
	APFL	通过推导全局模型和局部模型的一般边界找出最优混合参数
	SCAFFOLD	使用控制变量纠正本地更新中的“客户端漂移”问题
	Ditto	使用本地模型与全局模型的欧氏距离作为正则化项以鼓励个性化模型向全局最优模型靠近
	MOON	利用模型表示之间的相似性来纠正各个客户端的局部学习

分类	方法	参考文献
基于优化器的方法	Meta-Learner, LSTM, RL², LTL	[42-44]
基于记忆存储的方法	MANN, SNAIL	[45-46]
基于基础泛化模型的方法	MAML, iMAML, ESMAML、	[5, 47-48]
	Reptile, LEO	[50-51]
基于度量学习的方法	Meta-critic Network, Matching Network、	[52-56]
	Prototy Network, Siamses Network、
	Relation Network
基于数据增强的方法	Sill-Net, MetaAugment,	[61, 63-65]
	MetaGAN, MED

Federated meta learning: a review

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