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Federated Learning with Python

You're reading from Federated Learning with Python Design and implement a federated learning system and develop applications using existing frameworks

Product type Paperback

Published in Oct 2022

Publisher Packt

ISBN-13 9781803247106

Length 326 pages

Edition 1st Edition

Languages

Python

Tools

Federated Learning

Concepts

Design

Authors (2):

George Jeno

Kiyoshi Nakayama, PhD

View More author details

Table of Contents (17) Chapters

Preface

1. Part 1 Federated Learning – Conceptual Foundations

2. Chapter 1: Challenges in Big Data and Traditional AI FREE CHAPTER

3. Chapter 2: What Is Federated Learning?

4. Chapter 3: Workings of the Federated Learning System

5. Part 2 The Design and Implementation of the Federated Learning System

6. Chapter 4: Federated Learning Server Implementation with Python

7. Chapter 5: Federated Learning Client-Side Implementation

8. Chapter 6: Running the Federated Learning System and Analyzing the Results

9. Chapter 7: Model Aggregation

10. Part 3 Moving Toward the Production of Federated Learning Applications

11. Chapter 8: Introducing Existing Federated Learning Frameworks

12. Chapter 9: Case Studies with Key Use Cases of Federated Learning Applications

13. Chapter 10: Future Trends and Developments

14. Index

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15. Other Books You May Enjoy

Appendix: Exploring Internal Libraries

Overview of the internal libraries for the FL system

Figure A.1 shows the Python code components for the internal libraries found in the lib/util folder of the fl_main directory, which is used in the database, aggregator, and agent of the FL system:

Figure A.1 – Python software components for the internal libraries used in the database, aggregator, and agent

The following are brief descriptions of the Python files for the internal libraries found in the lib/util folder of the FL system.

states.py

The states.py file in the lib/util folder defines a variety of enumeration classes to support implementing the FL system. Definitions of the classes include FL client states, types of ML models and messages, and locations of the information and values of various messages.

communication_handler.py

The communication_handler.py file in the lib/util folder can provide communication functionalities among the database, FL server, and clients, mainly defining...

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Authors (2)

Kiyoshi Nakayama, PhD

Kiyoshi Nakayama, PhD

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 world's 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.

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George Jeno

George Jeno

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 master's degree in computer science (with a specialization in machine learning) from Georgia Tech.

See other products by George Jeno

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