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

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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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Appendix: Exploring Internal Libraries

Understanding the FL system flow – from initialization to continuous operation

Each distributed agent belongs to an aggregator that is managed by an FL server, where ML model aggregation is conducted to synthesize a global model that is going to be sent back to the agents. An agent uses its local data to train an ML model and then uploads the trained model to the corresponding aggregator. The concept sounds straightforward, so we will look into a bit more detail to realize the entire flow of those processes.

We also define a cluster global model, which we simply call a cluster model or global model, which is an aggregated ML model of local models collected from distributed agents.

Note

In the next two chapters, we will guide you on how to implement the procedure and sequence of messages discussed in this chapter. However, some of the system operation perspectives, such as an aggregator or agent system registration in the database, are not introduced in the code sample...

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

Table of Contents (17) Chapters

Understanding the FL system flow – from initialization to continuous operation

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Authors (2)

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