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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

Running the Federated Learning System and Analyzing the Results

In this chapter, you will run the federated learning (FL) system that has been discussed in previous chapters and analyze the system behaviors and the outcomes of the aggregated models. We will start by explaining the configuration of the FL system components in order to run the systems properly. Basically, after installing the simple FL system provided by our GitHub sample, you first need to pick up the server machines or instances to run the database and aggregator modules. Then, you can run agents to connect to the aggregator that is already running. The IP address of the aggregator needs to be correctly set up in each agent-side configuration. Also, there is a simulation mode so that you can run all the components on the same machine or laptop to just test the functionality of the FL system. After successfully running all the modules of the FL system, you will be able to see the data folder and a database created under...

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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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