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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

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Product type Paperback
Published in Oct 2022
Publisher Packt
ISBN-13 9781803247106
Length 326 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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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
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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

Local ML engine integration into an FL system

The successful integration of FL client libraries into a local ML engine is key to conducting FL in distributed environments later on.

The minimal_MLEngine.py file in the examples/minimal directory found in the GitHub repository at https://github.com/tie-set/simple-fl, as shown in Figure 5.2, provides an example of integrating FL client-side libraries into a minimal ML engine package:

Figure 5.2 – The minimal ML engine package

Next, we will explain what libraries need to be imported into the local ML engine in the following section.

Importing libraries for a local ML engine

The following code shows the importing process, where general libraries such as numpy, time, and Dict are imported first. The key part of this process is that Client is imported from the client.py file in the fl_main.agent folder. This way, a developer does not need to know too much about the code inside an FL system and just...

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