What this book covers
Chapter 1, Challenges in Big Data and Traditional AI, is all about orienting you to understand the current issues with big data systems and the traditional centralized machine learning approach and how FL could resolve those problems, such as data privacy, bias and silos, model drifts, and performance degradation. This will prepare you to dive deeper into the design and implementation of the FL system.
Chapter 2, What Is Federated Learning?, continues the introduction to FL and will help you understand the FL concept with the current trend of distributed learning as well as machine learning basics. You will also learn about the benefits of FL as a model-centric machine learning approach in terms of data privacy and security as well as efficiency and scalability.
Chapter 3, Workings of the Federated Learning System, gives you a solid understanding of how the FL system will work and interact among distributed components within the FL system itself. You will learn about the fundamental architecture of the FL system together with its procedure flow, state transition, and sequence of messaging toward the continuous operation of the FL system.
Chapter 4, Federated Learning Server Implementation with Python, guides you through learning the implementation principles of the FL server-side system, including database server and aggregator modules. The chapter also talks about communication among the FL components and model aggregation, as well as how to manage the states of the distributed learning agents and local and global models.
Chapter 5, Federated Learning Client-Side Implementation, describes the FL client-side functionalities as well as libraries that can be used by local machine learning engines and processes. The core client-side functionalities include registration of a distributed learning agent with the FL platform, global model receipt, and local model upload and sharing.
Chapter 6, Running the Federated Learning System and Analyzing the Results, will help you run a simplified FL framework to understand the behavior of the FL system and procedure as well as the most standard model aggregation method of federated averaging. You will also be able to validate the outcome of running the FL framework by analyzing the results of two example test cases.
Chapter 7, Model Aggregation, is an important chapter to understand model aggregation, which is the foundation of FL. You will understand how different characterizations of an FL scenario call for various aggregation methods and should have an idea of how these algorithms can actually be implemented.
Chapter 8, Introducing Existing Federated Learning Frameworks, explains the existing FL projects and frameworks, such as PySyft, TFF, Flower, OpenFL, and STADLE, and their APIs. There are many useful FL projects with different design philosophies, and you will understand the features and differences among those frameworks.
Chapter 9, Case Studies with Key Use Cases of Federated Learning Applications, introduces some of the major use cases of FL in different industries. You’ll become familiar with some of the applications of FL in various fields, such as medical and healthcare, the financial sector, edge computing, the Internet of Things, and distributed learning for big data, in which FL has shown significant potential to overcome many important technological challenges.
Chapter 10, Future Trends and Developments, describes the future direction of AI technologies driven by ongoing research and development of FL. You will be introduced to the new perspective of the Internet of Intelligence and wisdom-centric platforms. Thus, you will be ready to welcome the world of collective intelligence.
Appendix, Exploring Internal Libraries, provides an overview of internal libraries, including enumeration classes for implementing the FL systems, communication protocol, data structure handler, and helper and supporting functions.
