Federated Learning with Python

Learn how to design and deploy federated learning systems with Python in this comprehensive guide. You'll explore the challenges of centralized machine learning, understand the fundamentals of federated learning, and gain the skills to build scalable, private, and efficient distributed AI applications.

What this Book will help me do

• Understand the benefits and challenges of federated learning compared to traditional centralized machine learning.
• Learn the theoretical and conceptual foundations of federated learning.
• Design and develop a federated learning system architecture suitable for real-world applications.
• Implement federated learning systems and familiarize yourself with client-server interactions and data aggregation methods.
• Explore real-world case studies and future trends to effectively apply federated learning in various industries.

Author(s)

Kiyoshi Nakayama, PhD, is an accomplished researcher in distributed systems and machine learning with academic and industry experience. George Jeno is a hands-on technologist specializing in AI system implementation. Together, they provide a balanced perspective combining theoretical insights with practical applications.

Who is it for?

This book is designed for machine learning engineers, data scientists, and AI professionals eager to delve into federated learning. It is suitable for those with a working knowledge of Python and machine learning who aspire to build systems that preserve data privacy and scalability. The content progresses from foundational concepts to implementation, catering to learners aiming to apply federated learning in practical scenarios.