Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletter Hub
Free Learning
Arrow right icon
timer SALE ENDS IN
0 Days
:
00 Hours
:
00 Minutes
:
00 Seconds
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

Arrow left icon
Profile Icon Kiyoshi Nakayama, PhD Profile Icon George Jeno
Arrow right icon
£9.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (12 Ratings)
Paperback Oct 2022 326 pages 1st Edition
eBook
£28.99
Paperback
£35.99
Subscription
Free Trial
Renews at £9.99p/m
Arrow left icon
Profile Icon Kiyoshi Nakayama, PhD Profile Icon George Jeno
Arrow right icon
£9.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (12 Ratings)
Paperback Oct 2022 326 pages 1st Edition
eBook
£28.99
Paperback
£35.99
Subscription
Free Trial
Renews at £9.99p/m
eBook
£28.99
Paperback
£35.99
Subscription
Free Trial
Renews at £9.99p/m

What do you get with a Packt Subscription?

Free for first 7 days. £13.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

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

Country selected
Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 28, 2022
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781803247106
Category :
Languages :
Concepts :

What do you get with a Packt Subscription?

Free for first 7 days. £13.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details

Publication date : Oct 28, 2022
Length: 326 pages
Edition : 1st
Language : English
ISBN-13 : 9781803247106
Category :
Languages :
Concepts :

Packt Subscriptions

See our plans and pricing
Modal Close icon
£9.99 billed monthly
Feature tick icon Unlimited access to Packt's library of 6,500+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Simple pricing, no contract
£99.99 billed annually
Feature tick icon Unlimited access to Packt's library of 6,500+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just £5 each
Feature tick icon Exclusive print discounts
£139.99 billed in 18 months
Feature tick icon Unlimited access to Packt's library of 6,500+ practical books and videos
Feature tick icon Constantly refreshed with 50+ new titles a month
Feature tick icon Exclusive Early access to books as they're written
Feature tick icon Solve problems while you work with advanced search and reference features
Feature tick icon Offline reading on the mobile app
Feature tick icon Choose a DRM-free eBook or Video every month to keep
Feature tick icon PLUS own as many other DRM-free eBooks or Videos as you like for just £5 each
Feature tick icon Exclusive print discounts

Frequently bought together


Stars icon
Total £ 111.97
Machine Learning Techniques for Text
£35.99
Federated Learning with Python
£35.99
Modern Time Series Forecasting with Python
£39.99
Total £ 111.97 Stars icon
Visually different images

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(12 Ratings)
5 star 91.7%
4 star 8.3%
3 star 0%
2 star 0%
1 star 0%
Filter icon Filter
Top Reviews

Filter reviews by




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
Get free access to Packt library with over 7500+ books and video courses for 7 days!
Start Free Trial

FAQs

What is included in a Packt subscription? Chevron down icon Chevron up icon

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription? Chevron down icon Chevron up icon

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits? Chevron down icon Chevron up icon

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled? Chevron down icon Chevron up icon

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title? Chevron down icon Chevron up icon

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team. 

Can I download the code files for Early Access titles? Chevron down icon Chevron up icon

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date? Chevron down icon Chevron up icon

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready? Chevron down icon Chevron up icon

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access? Chevron down icon Chevron up icon

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered? Chevron down icon Chevron up icon

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content? Chevron down icon Chevron up icon

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access? Chevron down icon Chevron up icon

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.