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

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Kiyoshi Nakayama PhD Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks
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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 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, youll 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 FLs conceptual framework or theory, as is the case with the majority of research-related literature.

By the end of this book, youll 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.

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
Who this book is 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. Youll need basic knowledge of Python programming and machine learning concepts to get started with this book.

Table of Contents
  1. Challenges in Big Data and Traditional AI
  2. What Is Federated Learning?
  3. Workings of the Federated Learning System
  4. Federated Learning Server Implementation with Python
  5. Federated Learning Client-Side Implementation
  6. Running the Federated Learning System and Analyzing the Results
  7. Model Aggregation
  8. Introducing Existing Federated Learning Frameworks
  9. Case Studies with Key Use Cases of Federated Learning Applications
  10. Future Trends and Developments
  11. Appendix, Exploring Internal Libraries

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Kiyoshi Nakayama PhD - Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks
Federated Learning with Python: Design and implement a federated learning system and develop applications using existing frameworks
Kiyoshi Nakayama PhD
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 - photo 1
Federated Learning with Python

Design and implement a federated learning system and develop applications using existing frameworks

Kiyoshi Nakayama, PhD

George Jeno

BIRMINGHAMMUMBAI Federated Learning with Python Copyright 2022 Packt Publishing - photo 2

BIRMINGHAMMUMBAI

Federated Learning with Python

Copyright 2022 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

Publishing Product Manager: Dinesh Chaudhary

Senior Editor: Nathanya Dias

Content Development Editor: Shreya Moharir

Technical Editor: Devanshi Ayare

Copy Editor: Safis Editing

Project Coordinator: Farheen Fathima

Proofreader: Safis Editing

Indexer: Manju Arasan

Production Designer: Roshan Kawale

Marketing Coordinators: Shifa Ansari

First published: October 2022

Production reference: 1141022

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham

B3 2PB, UK.

ISBN 978-1-80324-710-6

www.packt.com

Acknowledgments

We would like to thank Dr. Norikazu Furukawa for contributing to Chapter 1, Challenges in Big Data and Traditional AI, and Anthony Maddalone for contributing to Chapter 9, Case Studies with Key Use Cases of Federated Learning Applications, with their great insight into current trends, challenges, and ongoing efforts in the machine learning field and also its future direction. We also thank Dr. Genya Ishigaki for his contribution to the code of the GitHub repository used throughout this book. We acknowledge the contribution of Dr. Jose Barreiros to the content related to the robotics use case.

Contributors
About the authors

Kiyoshi Nakayama, PhD, is the founder and CEO of TieSet Inc., which leads the development and dissemination of one of the most advanced distributed and federated learning platforms in the world. Before founding TieSet, he was a research scientist at NEC Laboratories America, renowned for having the worlds top-notch machine learning research group of researchers. He was also a postdoctoral researcher at Fujitsu Laboratories of America, where he implemented a distributed system for smart energy. He has published several international articles and patents and received the best paper award twice in his career. Kiyoshi received his PhD in computer science from the University of California, Irvine.

George Jeno is a co-founder of TieSet Inc. and has been a tech lead for the development of the STADLE federated learning platform. He has a deep understanding of machine learning theory and system architecture design, and he has leveraged this knowledge to research new algorithms and applications for distributed and federated learning. He holds a masters degree in computer science (with a specialization in machine learning) from Georgia Tech.

About the reviewer

Sougata Pal is a passionate technology specialist, working as an enterprise architect in software architecture design and application scalability management, team building, and management. With over 15 years of experience, she has worked with different start-ups and large-scale enterprises to develop their business application infrastructures, enhancing their reach to customers. Having contributed to different open source projects on GitHub to empower the open source community, for the last couple of years, Sougata has been playing around with federated learning and cybersecurity algorithms to enhance the performance of cybersecurity processes by introducing concepts of federated learning.

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