Federated Learning with Python
Design and implement a federated learning system and develop applications using existing frameworks
Kiyoshi Nakayama PhD, George Jeno
- 326 pages
- English
- ePUB (adapté aux mobiles)
- Disponible sur iOS et Android
Federated Learning with Python
Design and implement a federated learning system and develop applications using existing frameworks
Kiyoshi Nakayama PhD, George Jeno
Ă propos de ce livre
Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next levelKey Featuresâą Design distributed systems that can be applied to real-world federated learning applications at scaleâą Discover multiple aggregation schemes applicable to various ML settings and applicationsâą Develop a federated learning system that can be tested in distributed machine learning settingsBook DescriptionFederated 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 learnâą Discover the challenges related to centralized big data ML that we currently face along with their solutionsâą Understand the theoretical and conceptual basics of FLâą Acquire design and architecting skills to build an FL systemâą Explore the actual implementation of FL servers and clientsâą Find out how to integrate FL into your own ML applicationâą Understand various aggregation mechanisms for diverse ML scenariosâą Discover popular use cases and future trends in FLWho this book is forThis 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.