Robust and Communication-Efficient Federated Learning from Non-IID Data

Local Model Poisoning Attacks to Byzantine-Robust Federated Learning

In federated learning, multiple client devices jointly learn a machine learning model: each client device maintains a local model for its local training dataset, while a master device maintains a global model via aggregating the local models from the…

Advances and Open Problems in Federated Learning

挖个大坑,等有空了再回来填.心心念念的大综述呀(吐血三升)! 郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 项目地址:https://github.com/open-intelligence/federated-learning-chinese 具体内容参见项目地址,欢迎大家在项目的issue上提出问题!!! Abstract 联邦学习(FL)是一种机器学习环境,其中许多客户端(如移动设备或整个组织)在中央服务器(如服务提供商)的协调下协同训练模型,同时保持训练数据去中心化.FL…

Federated Learning: Challenges, Methods, and Future Directions

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! arXiv:1908.07873v1 [cs.LG] 21 Aug 2019 Abstract 联邦学习包括通过远程设备或孤立的数据中心(如移动电话或医院)训练统计模型,同时保持数据本地化.在异构和潜在的大规模网络中进行训练带来了新的挑战,这些挑战的要求从根本上偏离了大规模机器学习.分布式优化和隐私保护数据分析的标准方法.在这篇文章中,我们讨论了联邦学习的独特特点和挑战,对当前的方法进行了广泛的概述,并概述了与广泛的研究界相关的未来工…

联邦学习（Federated Learning）

联邦学习简介联邦学习(Federated Learning)是一种新兴的人工智能基础技术,在 2016 年由谷歌最先提出,原本用于解决安卓手机终端用户在本地更新模型的问题,其设计目标是在保障大数据交换时的信息安全.保护终端数据和个人数据隐私.保证合法合规的前提下,在多参与方或多计算结点之间开展高效率的机器学习.其中,联邦学习可使用的机器学习算法不局限于神经网络,还包括随机森林等重要算法.联邦学习有望成为下一代人工智能协同算法和协作网络的基础. 联邦学习的系统构架以包…

联邦学习 Federated Learning 相关资料整理

本文链接:https://blog.csdn.net/Sinsa110/article/details/90697728代码微众银行+杨强教授团队的联邦学习FATE框架代码:https://github.com/WeBankFinTech/FATE谷歌联邦迁移学习TensorFlow Federated (TFF)框架代码:https://www.tensorflow.org/federated/论文Towards Federated Learning at Scale: System Desi…

Overcoming Forgetting in Federated Learning on Non-IID Data

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 以下是对本文关键部分的摘抄翻译,详情请参见原文. NeurIPS 2019 Workshop on Federated Learning for Data Privacy and Confidentiality, Vancouver, Canada. Abstract 我们解决了非i.i.d.情况下的联邦学习问题,在这种情况下,局部模型漂移,抑制了学习.基于与终身学习的类比,我们将灾难性遗忘的解决方案改用在联邦学习上.我们在损失函数中…

Reliable Federated Learning for Mobile Networks

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 以下是对本文关键部分的摘抄翻译,详情请参见原文. arXiv: 1910.06837v1 [cs.CR] 14 Oct 2019 Abstract 联邦学习是一种很有前途的机器学习方法,它利用来自多个节点(如移动设备)的分布式个性化数据集来提高性能,同时为移动用户提供隐私保护.在联邦学习中,训练数据广泛分布在移动设备上,作为用户得到维护.中央聚合方通过使用移动设备的本地训练数据从移动设备收集本地更新来更新全局模型,以在每次迭代中训练全…

Robust Pre-Training by Adversarial Contrastive Learning

目录概主要内容代码 Jiang Z., Chen T., Chen T. & Wang Z. Robust Pre-Training by Adversarial Contrastive Learning. NIPS, 2020. 概本文介绍了一种利用对比学习进行对抗预训练的方法. 主要内容思想是很简单的, 就是利用对比学习进行训练(样本的augumentation多一个\(\delta\)), 然后再通过此方法训练得到的参数进行finetune. 比较特别的是, 有三种预训练的方案:…

【流行前沿】联邦学习 Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits

Sunwoo Lee, , Anit Kumar Sahu, Chaoyang He, and Salman Avestimehr. "Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits." (2022). 简介传统FedAvg算法下,SGD的多轮本地训练会导致模型差异增大,从而使全局loss收敛缓慢.本文作者提出每次本地用户更新后,仅对部分网络参数进行聚合,从而降低模型…

【流行前沿】联邦学习 Federated Learning with Only Positive Labels

核心问题:如果每个用户只有一类数据,如何进行联邦学习? Felix X. Yu, , Ankit Singh Rawat, Aditya Krishna Menon, and Sanjiv Kumar. "Federated Learning with Only Positive Labels." (2020). 简述在联邦学习中,如果每个用户节点上只有一类数据,那么在本地训练时会将任何数据映射到对应标签,此时使用分布式SGD或FedAvg算法学习分类器会导致整体学习失效.为了安全性…

【论文笔记】A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond（综述）

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond Author Sawsan AbdulRahman, Hanine Tout, Hakima Ould-Slimane, Azzam Mourad, Chamseddine Talhi, Mohsen Guizani Keywords AI; DL; distributed intellig…

【论文笔记】A review of applications in federated learning（综述）

A review of applications in federated learning Authors Li Li, Yuxi Fan, Mike Tse, Kuo-Yi Lin Keywords Federated learning; Literature review; Citation analysis; Research front Abstract FL是一种协作地分散式隐私保护技术,它的目标是克服数据孤岛与数据隐私的挑战.本研究旨在回顾目前在工业工程中的应用,以指导未来的落地应…

【论文笔记】Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges（综述）

Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges Authors Solmaz Niknam, Harpreet S. Dhillon, Jeffrey H. Reed Keywords Abstract 本文介绍了FL的总体思路,讨论了在5G网络中可能的应用,描述了无线通信环境中的关键技术挑战与关于未来研究的开放性问题. Publication DATA SCIEN…

Learning Spark: Lightning-Fast Big Data Analysis 中文翻译

Learning Spark: Lightning-Fast Big Data Analysis 中文翻译行为纯属个人对于Spark的兴趣,仅供学习. 如果我的翻译行为侵犯您的版权,请您告知,我将停止对此书的开源翻译. Translation the book of Learning Spark: Lightning-Fast Big Data Analysis is only for spark developer educational purposes. If I violated you…

少标签数据学习：宾夕法尼亚大学Learning with Few Labeled Data

目录 Few-shot image classification Three regimes of image classification Problem formulation A flavor of current few-shot algorithms How well does few-shot learning work today? The key idea Transductive Learning An example Results on benchmark datasets…

Robust and Communication-Efficient Federated Learning from Non-IID Data

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 以下是对本文关键部分的摘抄翻译,详情请参见原文. arXiv:1903.02891v1 [cs.LG] 7 Mar 2019 Abstract 联邦学习允许多个参与方在其整合数据上联合训练一个深度学习模型,而无需任何参与方将其本地数据透露给一个集中的服务器.然而,这种形式的隐私保护协作学习的代价是训练期间的大量通信开销.为了解决这个问题,分布式训练文献中提出了几种压缩方法,这些方法可以将所需的通信量减少三个数量级.然而,这些现有的方法…

Federated Learning with Matched Averaging

挖个坑吧,督促自己仔细看一遍论文(ICLR 2020),看看自己什么时候也能中上那么一篇(流口水)~ 郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Abstract 联邦学习允许边缘设备协同学习共享模型,同时将训练数据保留在设备上,将模型训练能力与将数据存储在云中的需求分离开来.针对例如卷积神经网络(CNNs)和LSTMs等的现代神经网络结构的联邦学习问题,我们提出了联邦匹配平均(FedMA)算法.FedMA通过对提取到的具有相似特征的隐元素(即卷积层的通道:LSTM的隐状态:全…

CoRR 2015 | MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

MXNet是一个支持多种编程语言的机器学习库,使用MXNet可以方便地实现机器学习算法,尤其是深度神经网络.通过嵌入在宿主语言中,它将声明式符号表达与命令式张量计算相结合.它提供自动求导以计算梯度.MXNet具有高效的计算和存储操作,可运行在从移动设备到分布式GPU集群的各种异构系统上.MXNet的源代码可以在这里获得. 本文描述了MXNet的API设计和系统实现,并解释了如何以统一的方式处理符号表达式和张量操作的嵌入.我们的初步实验揭示了使用多GPU机器训练的大规模深度神经网络的应是有希望的.…

SCAFFOLD: Stochastic Controlled Averaging for On-Device Federated Learning

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 以下是对本文关键部分的摘抄翻译,详情请参见原文. arXiv:1910.06378v1 [cs.LG] 14 Oct 2019 Abstract 联邦学习是现代大规模机器学习中的一个关键场景.在这种情况下,训练数据仍然分布在大量的客户机上,这些客户机可能是电话.其他移动设备或网络传感器,并且在不通过网络传输客户机数据的情况下学习集中式模型.此方案中使用的标准优化算法是联邦平均(FedAvg).然而,当客户端数据是异质的(这在应用程序中…

data mining，machine learning，AI，data science，data science，business analytics

数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)的区别是什么? 数据科学(data science)和商业分析(business analytics)之间有什么关系? 本来我以为不需要解释这个问题的,到底数据挖掘(data mining),机器学习(machine learning),和人工智能(AI)有什么区别,但是前几天因为有个学弟问我,我想了想发现我竟然也回答不出来,我在知乎和博客上查了查这个问题,发现还没有人写过比较详细和有说服力的对比…

Learning Experience of Big Data: Deploying Tomcat 8.0 and connect ssh without password

This mission seems to be easier--we can just decompression Tomcat to our virtural machine and deploy ssh to login without password.Login ssh without password is very critical,although distributed Systems can also run without it.But,as we know,distrib…

Learning Experience of Big Data: Connect CentOs to Xshell and set Java environment on CentOS

1.set up connections between vitural machine and Xshell: After we connect the virtural machine to network,we can used an application named "Xshell" to operate the machine instead of the inconvenient way to operate it.Definitely,we should connect…

Learning Experience of Big Data:The First Day-Try to set up a network connection on my virtural machine

After we install our virtual machine,the first thing we should do is to set up a network connection on it.After searching in the browser,I found that the settings of network are placed in a file named ifcfg-eth,so we use the vi editor to edit it like…

Learning Experience of Big Data: Learn to install CentOs 6.5 on my laptop

I have learnt some experience about Big Data during my summer vocation,I was told that The first thing to start Big Data is to install a Linux system on my laptop,that is what I'm going to discuss about.The system which I have used was CentOs 6.5,bes…

Federated Machine Learning: Concept and Applications

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated Machine Learning: Concept and Applications. ACM Trans. Intell. Syst. Technol. 10, 2, Article 12 (February 2019), 19 pages. https://doi.org/0000001.0…

How do I learn machine learning?

https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644 How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…

Machine Learning and Data Mining（机器学习与数据挖掘）

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…

[C2P3] Andrew Ng - Machine Learning

##Advice for Applying Machine Learning Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the le…

Communication-Efficient Learning of Deep Networks from Decentralized Data

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA. JMLR: W&CP volume 54. Copyright 2017 by the author(s). Abstract 现代移动设备可以访问大量适合模型学…

The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near

The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near July 27, 2015July 27, 2015 Tim Dettmers Deep Learning, NeuroscienceDeep Learning, dendritic spikes, high performance computing, neuroscience, singula…

【Robust and Communication-Efficient Federated Learning from Non-IID Data】的更多相关文章