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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

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

View More author details

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

Why subscribe?

15. Other Books You May Enjoy

Appendix: Exploring Internal Libraries

Further reading

The following are some sources if you wish to dive deeper into some concepts discussed in this chapter:

UNDERSTANDING THE TYPES OF FEDERATED LEARNING, posted by OpenMinded:
Thapa, Chandra, et al. SplitFed: When Federated Learning Meets Split Learning, Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 8. 2022:
SWARM LEARNING: TURN YOUR DISTRIBUTED DATA INTO COMPETITIVE EDGE, technical white paper:
Paritosh Ramanan and Kiyoshi Nakayama. BAFFLE: Blockchain based aggregator free federated learning, 2020 IEEE International Conference on Blockchain (Blockchain). IEEE, 2020: https://arxiv.org/pdf/1909.07452.pdf

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Authors (2)

Kiyoshi Nakayama, PhD

Kiyoshi Nakayama, PhD

Kiyoshi Nakayama, PhD, is the founder and CEO of TieSet Inc., which leads the development and dissemination of one of the most advanced distributed and federated learning platforms in the world. Before founding TieSet, he was a research scientist at NEC Laboratories America, renowned for having the world's top-notch machine learning research group of researchers. He was also a postdoctoral researcher at Fujitsu Laboratories of America, where he implemented a distributed system for smart energy. He has published several international articles and patents and received the best paper award twice in his career. Kiyoshi received his PhD in computer science from the University of California, Irvine.

See other products by Kiyoshi Nakayama, PhD

George Jeno

George Jeno

George Jeno is a co-founder of TieSet Inc. and has been a tech lead for the development of the STADLE federated learning platform. He has a deep understanding of machine learning theory and system architecture design, and he has leveraged this knowledge to research new algorithms and applications for distributed and federated learning. He holds a master's degree in computer science (with a specialization in machine learning) from Georgia Tech.

See other products by George Jeno

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