Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level

Key Features

• Design distributed systems that can be applied to real-world federated learning applications at scale
• Discover multiple aggregation schemes applicable to various ML settings and applications
• Develop a federated learning system that can be tested in distributed machine learning settings

Book Description

Federated learning (FL) is a paradigm-shifting technology in AI that enables and accelerates machine learning (ML), allowing you to work on private data. It has become a must-have solution for most enterprise industries, making it a critical part of your learning journey. This book helps you get to grips with the building blocks of FL and how the systems work and interact with each other using solid coding examples.

FL is more than just aggregating collected ML models and bringing them back to the distributed agents. This book teaches you about all the essential basics of FL and shows you how to design distributed systems and learning mechanisms carefully so as to synchronize the dispersed learning processes and synthesize the locally trained ML models in a consistent manner. This way, you'll be able to create a sustainable and resilient FL system that can constantly function in real-world operations. This book goes further than simply outlining FL's conceptual framework or theory, as is the case with the majority of research-related literature.

By the end of this book, you'll have an in-depth understanding of the FL system design and implementation basics and be able to create an FL system and applications that can be deployed to various local and cloud environments.

What you will learn

• Discover the challenges related to centralized big data ML that we currently face along with their solutions
• Understand the theoretical and conceptual basics of FL
• Acquire design and architecting skills to build an FL system
• Explore the actual implementation of FL servers and clients
• Find out how to integrate FL into your own ML application
• Understand various aggregation mechanisms for diverse ML scenarios
• Discover popular use cases and future trends in FL

Who this book is for

This book is for machine learning engineers, data scientists, and artificial intelligence (AI) enthusiasts who want to learn about creating machine learning applications empowered by federated learning. You'll need basic knowledge of Python programming and machine learning concepts to get started with this book.

Table of Contents

1. Challenges in Big Data and Traditional AI
2. What Is Federated Learning?
3. Workings of the Federated Learning System
4. Federated Learning Server Implementation with Python
5. Federated Learning Client-Side Implementation
6. Running the Federated Learning System and Analyzing the Results
7. Model Aggregation
8. Introducing Existing Federated Learning Frameworks
9. Case Studies with Key Use Cases of Federated Learning Applications
10. Future Trends and Developments
11. Appendix, Exploring Internal Libraries

About the Author

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.

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.