在此背景下,联邦学习(Federated machine learning/Federated Learning)应时而生,为边缘计算的安全问题提供了解决方案。联邦学习是一个机器学习框架,在参与方使用加密后的私有数据进行运算,仅交换加密状态后的模型的参数、权重及梯度等特征,无需将原始数据移出本地,也无需将加密后的原始数据移动集中,即能帮助多个机构在满...
Keywords Federated learning; mobile edge networks; resource allocation; communication cost; data privacy; data security Abstract 由于其允许ML模型的协作训练和允许移动网络优化的DL,FL可以作为移动边缘网络中的使能技术进行服务。然而,在大规模和异构的移动边缘网络中,有着各种局限的异构设备参与其中,这带来了FL实现...
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges 1 INTRODUCTION 对于无线通信,采用ML进行系统设计和分析尤其具有吸引力,因为传统模型驱动的方法不够丰富以应对现代无线网络不断增长的复杂度与异构性。单独利用数据分析(例如被使用在模型驱动的通信系统设计中)的一种替代选择是使用...
步骤一:执行【联邦学习】有两个任务,先执行训练任务,再根据训练模型进行预测任务; 模型训练任务:TrustML/training 预测任务:TrustML/prediction 1、Alice侧创建训练任务,进行任务配置。 2、Bob侧在【通用计算-审批流程】审批任务。 3、Alice侧运行任务,【通用计算-模型管理】查看模型。 步骤二:创建【训练任务】,运行...
除了今年的ICML有个security and privacy of machine learning的workshop之外, 年底的NeurIPS也会有一个单独的federated learning的workshop. 今年的CVPR也有FL的一个tutorial, 包括一个由各大牛们推荐的新的会议 SysML[5]也开始关注很多FL相关的问题. 国内也有很多公司开始做FL了, 除了WeBank把这个用在金融上, 还有...
The spread of the Internet of Things (IoT) involving an uncountable number of applications, combined with the rise of Machine Learning (ML), has enabled the rapid growth of pervasive and intelligent systems in a variety of domains, including healthcare, environment, railway transportation and Indus...
Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the amount of research...
Apparatuses for non real-time (Non-RT) radio access network intelligence controller (RIC) services for machine learning (ML) in an open radio access network (O-RAN) and apparatuses for Near-RT RIC services are disclosed. The services include ML capability query, federated learning session ...
Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitatio
Learning)算法框架,而AAAI Fellow杨强教授与微众银行随后提出了基于“联邦学习”(Federated Learning)的系统...