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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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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

Ongoing research and developments in FL

We now talk about the ongoing research and development projects that are being taken place both in academia and industries around the world. Let’s start with the different types and approaches of FL, and move on to ongoing efforts to further enhance the FL framework.

Exploring various FL types and approaches

In this book, we have visited the most basic algorithms and design concepts of an FL system. In the real world, we need to dig a bit deeper into what types of FL frameworks are available to extract the best performance out of those algorithms. Depending on the data scenario and use cases, we have several approaches in FL, as follows:

  • Horizontal FL and vertical FL
  • Centralized FL and decentralized FL
  • Cross-silo FL and cross-device FL

Now, let’s look at each type of FL in the following sections.

Horizontal FL and vertical FL

Horizontal FL uses datasets with the same feature space or schema across...

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