Preface
Federated learning (FL) is becoming a paradigm-shifting technology in AI because it is often said that with the FL framework, it is the machine learning model that needs to move around across the Internet, not the data itself, for the intelligence to continuously evolve and grow. Therefore, people call FL a model-centric approach, compared to the traditional data-centric approach, thus it is considered a game-changing technology. The idea of the model-centric approach can create an intelligence-centric platform to pioneer the wisdom-driven world.
By adopting FL, you can overcome the challenges that big data AI has been facing for a long time, such as data privacy, training cost and efficiency, and delay in the delivery of the most updated intelligence. However, FL is not a magic solution to resolve all the issues in big data just by aggregating machine learning models blindly. We need to design the distributed systems and learning mechanisms very carefully to synchronize all the distributed learning processes and synthesize all the locally trained machine learning models consistently. That way, we can create a sustainable and resilient FL system that can continuously function even in a real operation at scale.
Therefore, this book goes beyond just describing the conceptual and theoretical aspects of FL as seen in many research projects with simulators or prototypes that have been introduced in most of the literature related to this field. Rather, you will learn about the entire design and implementation principles by looking into the codes of the simplified federated learning system to validate the workings and results of the framework.
By the end of this book, you will create your first application based on federated learning that can be installed and tested in various settings in both local and cloud environments.
