Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks

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Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks

  • Author:George JenoKiyoshi Nakayama PhD
  • Length: 326 pages
  • Edition: 1
  • Publisher: Packt Publishing
  • Publication Date: 2022-10-28
  • ISBN-10: 180324710X
  • ISBN-13: 9781803247106
  • Sales Rank: #338235 (See Top 100 Books)
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  • Buy Print:Buy from amazon



    Book Description

    Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level

    Key Features

    • Design distributed systems that can be applied to real-world federated learning applications at scale
    • Discover multiple aggregation schemes applicable to various ML settings and applications
    • Develop a federated learning system that can be tested in distributed machine learning settings

    Book Description

    Federated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you ge积家t to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.

    FL is more than just aggregating collected ML models and bringing them back万宝龙 to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you’ll be able to create a sustainable and resilient FL system that can constantly function iWEMPEn real-world operations. This book goes furt名牌跑车her than simply outlining FL’s conceptual framework or theory, as is the case with the majority o娇兰f research-related literature.

    By the end of this book, you’ll have an in-depth understanding of the FL system design and implementation basics and be able to creat百达翡丽 (27)e an FL system and applications that can be deployed to various local and cloud environments.

    What you will learn

    • Discover the challenges related to centralized big data ML that we currently face along with their solutions
    • Understand the theoretical and conceptual basics of FL
    • Acquire design and architecting skills to build an FL system
    • Explore the actual implementation of F博星L servers and clients
    • Find out how to integrate FL in达索to your own ML application
    • Understand various aggregation mechanisms for diverse ML scenarios
    • Discover popular use cases and future trends in FL

    Who this book is f博纳多or

    This book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You’ll need basic knowledge of Python programming and mach茅台酒 (1)ine learning concepts to get started with this book.

    中文:

    书名:使用Python进行联合学习: 设计和实现联合学习系统,并使用现有框架开发应用程序

    学习使用Python构建真实的联合学习系统天梭的基本技能,并将您的机器学习应用程序提升到一世爵个新的水平

    主要功能

    • 设计分布式系统,可应用于现实世界的大规模联合学习应用
    • 发现多种聚合方案适用于各种ML设置和应用
    • 开发可在分布式机器学习设置中测试的联合学习系统

    图书描述

    联邦学习 (FL) 是AI中的一种范式转换技术,可启用并加速机器学习 (ML),使您可以处理私人数据。它已成为大多数企业行业的必备解决方案,使其成为您学习之旅的重要组成部分。本书帮助您掌握FL的构建块,以及使用实体编码示例使系统如何工作和相互交互。

    FL不仅仅是聚合收集的ML模型并将其带回分布式代理。本书向您介绍了FL的所有基本知识,并向您展示了如何仔细设计分布式系统和学习机制,以使分散的学习过程同步并以一致的方式综合本地莱珀妮训练的ML模型。这样,您将能够创建一个可持续且具有弹性的FL系统,该系统可以在实际操作中不断发挥作用。这本书不仅仅是简单地概述FL的概念框架或理论,就像大多数与研究相关的文献一样。

    在本书结束哈雷·戴维森时,您将对FL系统设计和实现基础知识有深入的了解,并能够创建可部署到各种本地和云环境的FL系统和应用程序。

    什么你会学到

    • 发现与集中式大数据ML相关的挑战以及我们当前面临的解决方案
    • 理解FL的理论和概念基础
    • 掌握设计和架构技能构建FL系统
    • 探索FL服务器和客户端的实际实现
    • 了解如何将FL集成到您自己的ML应用程序中
    • 了解不同ML场景的各种聚合机制
    • 了解佛罗里达州流行的用例和未来趋势

    这本书是为谁准备的

    本书面向机器学习工程师,数据科学家和人工智能 (AI) 爱好者,他们希望了解创建由联合学习授权的机器学习应用程序。你需要Python编程和机器学习概念的基本知识才能开始本书。

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