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Federated Learning with Python
Federated Learning with Python

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

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Profile Icon Kiyoshi Nakayama, PhD Profile Icon George Jeno
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$37.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (12 Ratings)
eBook Oct 2022 326 pages 1st Edition
eBook
$37.99
Paperback
$46.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Kiyoshi Nakayama, PhD Profile Icon George Jeno
Arrow right icon
$37.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (12 Ratings)
eBook Oct 2022 326 pages 1st Edition
eBook
$37.99
Paperback
$46.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$37.99
Paperback
$46.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • 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

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 get 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 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 in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of 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 create an FL system and applications that can be deployed to various local and cloud environments.

Who is this book for?

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 machine learning concepts to get started with this book.

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 FL servers and clients
  • Find out how to integrate FL into your own ML application
  • Understand various aggregation mechanisms for diverse ML scenarios
  • Discover popular use cases and future trends in FL

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 28, 2022
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781803248752
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Product Details

Publication date : Oct 28, 2022
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781803248752
Category :

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Frequently bought together


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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(12 Ratings)
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Dror Dec 30, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Federated Learning (FL) is an emerging, disruptive technology that radically changes the way enterprises in certain industries work with data and enable data privacy. If you're a data scientist or machine learning (ML) expert working on healthcare, finance or IoT applications, this book is a must-read. For all other ML practitioners, I highly recommend this book as the best resource on the market to get acquainted with the emerging and increasingly important technology of FL and get practical advice on how it can be implemented in real-world applications.The book goes both broadly and deeply into the different aspects of FL. It provides an in-depth coverage of the foundations of FL, the design and implementation of FL systems (both server- and client-side), and production-related aspects of FL. It also provides a nice overview of future trends in FL.In contrast to many other resources on FL, this book covers both theoretical and practical aspects of FL. It begins with the necessary foundations, and then guides the reader on building an application based on FL that can be deployed in either local or cloud environments.It will prove to be a highly useful resource for learning FL for any data scientist, ML engineer or AI practitioner with basic familiarity with ML and the Python programming language.Highly recommended!
Amazon Verified review Amazon
Steven Fernandes Feb 23, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This comprehensive guide provides readers with the necessary building blocks to design and implement distributed systems that can be applied to real-world federated learning applications at scale.
Amazon Verified review Amazon
YYY-SSS Feb 15, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
"Federated Learning with Python" is praised for its practical, accessible approach to federated learning, making it an excellent resource for both researchers and practitioners. The book stands out for its clarity in explaining complex concepts and offering a hands-on guide to developing federated learning applications using Python. It goes beyond theory, providing real-life examples, code snippets, and detailed algorithm explanations. This makes it invaluable for those interested in applying federated learning in real-world projects, addressing challenges such as performance optimization and data heterogeneity. It's highlighted as a must-read for anyone looking to explore federated learning's potential, ensuring readers can develop innovative, privacy-preserving applications.
Amazon Verified review Amazon
Amazon Customer Feb 02, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I recently finished reading "Federated Learning with Python" and wanted to provide a review of this informative book on an emerging machine learning approach. The author provides a comprehensive overview of federated learning, a distributed training method that enables multiple devices to collaboratively train a model without sharing raw data.The book effectively explains the core concepts of federated learning in an easy to understand manner. The chapters cover the motivations behind federated learning, the algorithms and system architectures, privacy and security considerations, and practical implementations. I appreciated the inclusion of Python code examples to demonstrate the workflows for training federated models, as this helped solidify my understanding.A key strength of the book is the diverse examples of potential applications across industries like healthcare, finance, robotics, and more. The author examines how federated learning could be applied to drones, humanoid robots, patient diagnostic models, fraud detection, and other uses cases to provide robust models while maintaining data privacy. I found these applied examples highly valuable in comprehending how impactful federated learning can be.Overall, I would highly recommend "Federated Learning with Python " to anyone looking to gain knowledge about this distributed on-device training approach. The book succeeds in explaining the techniques accessibly, providing guidance on real-world implementations, and demonstrating the far-reaching potential of federated learning through diverse examples. This is an important read for both ML practitioners and leaders in AI-driven companies who wish to leverage federated learning while navigating data privacy regulations.
Amazon Verified review Amazon
Edd Sep 12, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The BEST book to follow one of the hot topics coming up in the age of AI. If you're interested in actually understanding privacy-focused training of AI models, I highly recommend you to check it out. The author provides a clear and concise introduction, followed by practical implementation examples and code snippets in Python.
Amazon Verified review Amazon
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