联邦学习的公平性研究综述

doi:10.11959/j.issn.2096-0271.2022088

Abstract

Abstract:

Federated learning uses data from multiple participants to collaboratively train global models and has played an increasingly important role in recent years in facilitating inter-firm data collaboration.On the other hand, the federal learning training paradigm often faces the dilemma of insufficient data, so it is important to provide assurance of fairness to motivate more participants to contribute their valuable resources.This paper illustrates the issue of fairness in federated learning.Firstly, three classifications of fairness based on different equity goals, from model performance balance, contribution assessment equity, and elimination of group discrimination are proposed, and then we provide indepth introduction and comparison of existing fairness promotion methods, aiming to help researchers develop new fairness promotion methods.Finally, by dissecting the needs in the process of federal learning implementation, five directions for future federated learning fairness research are proposed.

Key words: federated learning, fairness, balance in performance, measure of contributions

CLC Number:

Zhitao ZHU, Shijing SI, Jianzong WANG, Ning CHENG, Lingwei KONG, Zhangcheng HUANG, Jing XIAO. A survey on the fairness of federated learning[J]. Big Data Research, 2024, 10(1): 62-85.

Figures/Tables 8

参考文献	技术方案	公平指标	优势	局限性	帕累托最优
Yang et al.^[9]	组合多臂老虎机	测试准确度	为弱势客户端提供参与机会	仅考虑参与频率
RBCS-F^[10]	上下文组合多臂老虎机	选择分布参数	实现了长期公平约束	牺牲训练效率	√
FMAB^[11]	多臂老虎机	-	考虑客户端抽样过程的不确定性	在探测与通信时产生损耗
FedCS^[8]	估计等待时间指导客户选择	聚合更新客户端数	考虑了设备异质性问题	依赖诚实且有评估能力的客户端
TRA^[12]	容忍丢包损失	测试准确度方差	保证了一定丢包比例下的个性化和公平性	依赖诚实且有评估能力的客户端
PRLC^[14]	本地补偿	拉动操作数	有更好的扩展性	容易遭遇模型发散问题	√
Fed-ZDA^[15]	本地补偿	测试准确度方差	零样本数据增强	耗费更多的算力与时间
AFL^[16]	最小化最大期望损失	验证准确度标准差	防止模型对任何特定客户的过度拟合	仅适用于小规模客户，缺乏灵活性
FedMGDA+^[17]	多目标优化	训练损失平均方差	不牺牲任何参与者性能	难以识别恶意客户端	√
FCFL^[18]	多目标优化	人口均等、机会均等	考虑所有客户端优化目标	只关注客户端级别的群体公平性	√
q-FFL^[19]	放大高损失客户端的损失	测试准确度方差	更均匀的精度分布	需要多次试验确定最佳权重分配参数且对异构数据效果差
α-FedAvg^[20]	提升较低准确度客户端的权重	Jain’s 指数	可通过算法在训练之前确定参数α的取值	无法抵御膨胀损失攻击
DRFL^[21]	由客户端损失动态调整权重	准确度均匀程度	更方便地调整公平参数	没有解决公平参数对不同数据集的适应问题
PG-FFL^[22]	强化学习	基尼系数	自适应学习聚合权重	训练成本较高
TrustFed^[24]	维持信誉分数剔除异常设备	-	引入区块链抵御对抗性攻击	增加传输数据量
FedProx^[26]	允许客户端训练个性化模型	收敛稳定性	有助于减少系统异质性的负面影响	-
Ditto^[78]	多任务学习	测试准确度标准差	插件式设计方便改造现有模型	增加计算量
FedFV^[30]	使用梯度投影缓和梯度冲突	测试准确度方差	避免牺牲部分客户端模型准确性	过时的梯度估计可能导致模型发散	√
Cali3F^[31]	梯度校正技术与更新共享	NDCG标准差	避免本地模型发散，提高推荐性能均匀性	在推荐性能上非最优
FD^[27]	随机丢弃子模型激活节点	-	降低了通信和本地计算成本	需要多次试验选取最佳暂退率	√
AFD^[28]	维护激活分数图生成子模型	-	自动选取激活比例	-	√
FedAMP^[29]	自适应消息传递框架	测试准确度	增量优化	效果依赖于深度神经网络提高了计算量

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公平性分类	分类标准	相关工作
表现均衡公平性	客户端选择	[8-15]
	权重分配	[16-25]
	个性化模型	[26-31]
贡献评估公平性	资源条件	[32-40]
	效用影响	[41-47]
	验证精度	[48-52]
模型公平性	不公平来源	[53-60]
	联邦学习优势	[61-64]
	公平与隐私	[18,65-73]

参考文献	关键技术	衡量标准	分配结果	要求诚实客户端	抗膨胀攻击
[33]	分级别训练	本地数据条件	分级模型	√
[34]	信誉机制	可信度、承诺水平	分级模型	√
[35]	契约理论	本地数据	参与机会	√
[36]	Stacklberg博弈	CPU功率	参与机会	√
[37]	拍卖理论	多种资源	参与机会	√
[38]	拍卖理论	学习质量	参与机会	√
[39]	拍卖理论	相互评估	参与机会	√	√
[40]	VCG机制	计算成本、数据质量	金钱激励	√
[41]	边际损失	特征重要性	-		√
[42,80]	边际损失	边际损失	-		√
[43,45-47]	夏普利值	梯度信息	-		√
[48]	采样测试方法	采样数据大小	-	√	√
[49]	动态收益共享	期望损失、等待时间	金钱激励	√
[50]	信誉机制	验证数据集上准确度	-		√
[51]	信誉机制	本地计算时间和数据集大小	-		√
[52]	信誉机制	验证数据集上准确度	-		√

A survey on the fairness of federated learning

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