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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
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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

Future Trends and Developments

Intelligence will drive the next generation of technologies, not big data. Big data systems have some issues, as discussed in Chapter 1, Challenges in Big Data and Traditional AI, and the world is gradually transitioning from the data-centric era to the intelligence-centric generation. Federated learning (FL) will play a core role in wisdom-driven technologies. Thus, the time is now to welcome the world of collective intelligence.

In this chapter, we will talk about the direction of future AI technologies that are driven by the paradigm shift happening with FL. For many AI fields, such as privacy-sensitive areas and fields requiring scalability in machine learning (ML), the benefits and potential of FL are already significant, mainly because of the privacy-preserving and distributed learning aspects that FL naturally supports with its design. You will then learn about the different types of FL as well as the latest development efforts in that area...

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