智能科学与技术学报 ›› 2022, Vol. 4 ›› Issue (1): 29-44.doi: 10.11959/j.issn.2096-6652.202218
杨强1,2, 童咏昕3, 王晏晟3, 范力欣1, 王薇3, 陈雷2, 王魏4, 康焱1
修回日期:
2022-03-04
出版日期:
2022-03-15
发布日期:
2022-03-01
作者简介:
杨强(1961− ),男,博士,深圳前海微众银行股份有限公司首席人工智能官,香港科技大学教授,主要研究方向为联邦学习、迁移学习、群体智能等基金资助:
Qiang YANG1,2, Yongxin TONG3, Yansheng WANG3, Lixin FAN1, Wei WANG3, Lei CHEN2, Wei WANG4, Yan KANG1
Revised:
2022-03-04
Online:
2022-03-15
Published:
2022-03-01
Supported by:
摘要:
群体智能是在互联网高速普及下诞生的人工智能新范式。然而,数据孤岛与数据隐私保护问题导致群体间数据共享困难,群体智能应用难以构建。联邦学习是一类新兴的打破数据孤岛、联合构建群智模型的重要方法。首先,介绍了联邦学习的基础概念以及其与群体智能的关系;其次,基于群体智能视角对联邦学习算法框架进行了分类,从隐私、精度与效率3个角度讨论了联邦学习算法优化技术;而后,阐述了基于线性模型、树模型与神经网络模型的联邦学习算法模型;最后,介绍了联邦学习代表性开源平台与典型应用,并对联邦学习研究进行总结展望。
中图分类号:
杨强, 童咏昕, 王晏晟, 等. 群体智能中的联邦学习算法综述[J]. 智能科学与技术学报, 2022, 4(1): 29-44.
Qiang YANG, Yongxin TONG, Yansheng WANG, et al. A survey on federated learning in crowd intelligence[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(1): 29-44.
表2
联邦学习算法模型"
算法 | 数据划分 | 适用模型 | 模型类型 | 隐私保护机制 |
VFL-LM[ | 纵向 | 线性模型 | 线性回归 | 同态加密 |
VFL-LR[ | 纵向 | 逻辑回归 | 同态加密 | |
IFCA[ | 横向 | 线性回归 | 无 | |
FSVRG[ | 横向 | 线性回归/逻辑回归 | 无 | |
PDTL[ | 横向 | 树模型 | 决策树 | 多方安全计算/差分隐私 |
Pivot[ | 纵向 | 决策树/随机森林 | 多方安全计算 | |
SecureBoost[ | 纵向 | 决策树 | 同态加密 | |
FRF[ | 纵向 | 随机森林 | 加密/混淆 | |
RevFRF[ | 纵向 | 随机森林 | 加密/混淆 | |
FedAvg[ | 横向 | 神经网络模型 | 多层感知机/卷积神经网络 | 无 |
FedProx[ | 横向 | 多层感知机/卷积神经网络 | 无 | |
PFNM[ | 横向 | 多层感知机 | 无 | |
FedMA[ | 横向 | 多层感知机/循环神经网络 | 无 | |
FedGKT[ | 横向 | 卷积神经网络 | 无 | |
IncFed[ | 横向 | 循环神经网络 | 无 |
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