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…
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…
How to handle Imbalanced Classification Problems in machine learning? from:https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/ Introduction If you have spent some time in machine learning and data science, you would have d…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
A Statistical View of Deep Learning (IV): Recurrent Nets and Dynamical Systems Recurrent neural networks (RNNs) are now established as one of the key tools in the machine learning toolbox for handling large-scale sequence data. The ability to specify…
Unsupervised learning, attention, and other mysteries Get notified when our free report “Future of Machine Intelligence: Perspectives from Leading Practitioners” is available for download. The following interview is one of many that will be included…
ICLR 2013 International Conference on Learning Representations May 02 - 04, 2013, Scottsdale, Arizona, USA ICLR 2013 Workshop Track Accepted for Oral Presentation Zero-Shot Learning Through Cross-Modal Transfer Richard Socher, Milind Ganjoo, Hamsa Sr…
About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been…
About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "s…
Awesome Reinforcement Learning A curated list of resources dedicated to reinforcement learning. We have pages for other topics: awesome-rnn, awesome-deep-vision, awesome-random-forest Maintainers: Hyunsoo Kim, Jiwon Kim We are looking for more contri…
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…
<Deep Learning> Ian Goodfellow Yoshua Bengio Aaron Courvill 关于此书Part One重难点的个人阅读笔记. 2.7 Eigendecomposition we decompose a matrix into a set of eigenvectors and eigenvalues. 特征值与特征向量: 应用非常广泛: 图像处理中的PCA方法,选取特征值最高的k个特征向量来表示一个矩阵,从而达到降维分析+特征显示的方法, 还有图像压缩…
Introduction to Learning to Trade with Reinforcement Learning http://www.wildml.com/2018/02/introduction-to-learning-to-trade-with-reinforcement-learning/ Thanks a lot to @aerinykim, @suzatweet and @hardmaru for the useful feedback! The academic Deep…
A Brief Overview of Deep Learning (This is a guest post by Ilya Sutskever on the intuition behind deep learning as well as some very useful practical advice. Many thanks to Ilya for such a heroic effort!) Deep Learning is really popular these days. B…
http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/ The academic Deep Learning research community has largely stayed away from the financial markets. Maybe that’s because the finance industry has a bad reputation,…
源码:https://github.com/cheesezhe/Coursera-Machine-Learning-Exercise/tree/master/ex5 Introduction: In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. 1. Regularized Lin…