Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

Arrow left icon
Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247106
Length 326 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Authors (2):
Arrow left icon
George Jeno George Jeno
Author Profile Icon George Jeno
George Jeno
Kiyoshi Nakayama, PhD Kiyoshi Nakayama, PhD
Author Profile Icon Kiyoshi Nakayama, PhD
Kiyoshi Nakayama, PhD
Arrow right icon
View More author details
Toc

Table of Contents (17) Chapters Close

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 15. Other Books You May Enjoy Appendix: Exploring Internal Libraries

Understanding what happens when the minimal example runs

Understanding the behavior of the entire FL system step by step will help you design applications with FL enabled and further enhance the FL system itself. Let us first look into what happens when we run just one agent by printing some procedures of the agent and aggregator modules.

Running just one minimal agent

Let’s run the minimal agent after running the database and aggregator servers and see what happens. When the agent is started with the minimal ML engine, you will see the following messages in the agent console:

# Agent-side Console Example
INFO:root:--- This is a minimal example ---
INFO:root:--- Agent initialized —
INFO:root:--- Your IP is xxx.xxx.1.101 ---

When the agent initializes the model to be used for FL, it shows this message, and if you look at the state file, it has entered the sending state, which will trigger sending models to the aggregator when the FL client is started...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image