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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

FL system considerations

This section mainly focuses on the multi-party computation aspects of FL, including theoretical security measures and full decentralization approaches. The goal of this section is for you to be aware of some of the more practical considerations that should be taken into account for practical FL applications.

Security for FL systems

Despite the nascency of the technology, experimental usage of FL has emerged in a few sectors. Specifically, anti-money laundering (AML) in the financial industry and drug discovery and diagnosis in the medical industry have seen promising results, as proofs of concepts in those fields have been successfully conducted by companies such as Consilient and Owkin. In AML use cases, banks can cooperate with one another to identify fraudulent transactions efficiently without sharing their account data; and hospitals can keep their patient data to themselves while improving ML models for detecting health issues.

These solutions...

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