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
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

Potential enhancements to the FL server

Here are some of the key potential enhancements to the FL server discussed in this chapter.

Redesigning the database

The database was intentionally designed with minimal table information in this book and needs to be extended, such as by having tables of the aggregator itself, agents, the initial base model, and the project info, among other things, in the database. For example, the FL system described here in this chapter does not support the termination and restart of the server and agent processes. Thus, the FL server implementation is not complete, as it loses most of the information when any of the systems is stopped or failed.

Automating the registry of an initial model

In order to simplify the explanation of the process of registering the initial model, we defined the layers of the ML models using model names. This registration of the model in the system can be automated so that just loading a certain ML model, such as PyTorch...

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