PhD / Jeno Federated Learning with Python

Design and implement a federated learning system and develop applications using existing frameworks
1. Auflage 2022
ISBN: 978-1-80324-875-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 326 Seiten

No detailed description available for "Federated Learning with Python".

Autoren/Hrsg.

PhD, Kiyoshi Nakayama

Jeno, George

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

Table of Contents - Challenges in Big Data and Traditional AI
- What Is Federated Learning?
- Workings of the Federated Learning System
- Federated Learning Server Implementation with Python
- Federated Learning Client-Side Implementation

- Running the Federated Learning System and Analyzing the Results
- Model Aggregation
- Introducing Existing Federated Learning Frameworks
- Case Studies with Key Use Cases of Federated Learning Applications
- Future Trends and Developments
- Appendix, Exploring Internal Libraries

Über Autor(innen)

PhD Kiyoshi Nakayama:

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.Jeno George:

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.