| Description |
1 online resource |
| Summary |
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. |
| Contents |
Cover -- Title Page -- Copyright and Credits -- Acknowledgments -- Contributors -- Table of Contents -- Preface -- Part 1 Federated Learning -- Conceptual Foundations -- Chapter 1: Challenges in Big Data and Traditional AI -- Understanding the nature of big data -- Definition of big data -- Big data now -- Triple-A mindset for big data -- Data privacy as a bottleneck -- Risks in handling private data -- Increased data protection regulations -- From privacy by design to data minimalism -- Impacts of training data and model bias -- Expensive training of big data -- Model bias and training data |
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Model drift and performance degradation -- How models can stop working -- Continuous monitoring -- the price of letting causation go -- FL as the main solution for data problems -- Summary -- Further reading -- Chapter 2: What Is Federated Learning? -- Understanding the current state of ML -- What is a model? -- ML -- automating the model creation process -- Deep learning -- Distributed learning nature -- toward scalable AI -- Distributed computing -- Distributed ML -- Edge inference -- Edge training -- Understanding FL -- Defining FL -- The FL process -- FL system considerations |
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Security for FL systems -- Decentralized FL and blockchain -- Summary -- Further reading -- Chapter 3: Workings of the Federated Learning System -- FL system architecture -- Cluster aggregators -- Distributed agents -- Database servers -- Intermediate servers for low computational agent devices -- Understanding the FL system flow -- from initialization to continuous operation -- Initialization of the database, aggregator, and agent -- Initial model upload process by initial agent -- Overall FL cycle and process of the FL system -- Synchronous and asynchronous FL |
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The aggregator-side FL cycle and process -- The agent-side local retraining cycle and process -- Model interpretation based on deviation from baseline outputs -- Basics of model aggregation -- What exactly does it mean to aggregate models? -- FedAvg -- Federated averaging -- Furthering scalability with horizontal design -- Horizontal design with semi-global model -- Distributed database -- Asynchronous agent participation in a multiple-aggregator scenario -- Semi-global model synthesis -- Summary -- Further reading -- Part 2 The Design and Implementation of the Federated Learning System |
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Chapter 4: Federated Learning Server Implementation with Python -- Technical requirements -- Main software components of the aggregator and database -- Aggregator-side codes -- lib/util codes -- Database-side code -- Toward the configuration of the aggregator -- Implementing FL server-side functionalities -- Importing libraries for the FL server -- Defining the FL Server class -- Initializing the FL server -- Registration function of agents -- The server for handling messages from local agents -- The global model synthesis routine -- Functions to send the global models to the agents |
| Subject |
Machine learning.
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Python (Computer program language)
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Apprentissage automatique. |
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Python (Langage de programmation) |
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Machine learning |
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Python (Computer program language) |
| Added Author |
Jeno, George.
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| Other Form: |
Print version: 180324710X 9781803247106 (OCoLC)1346946432 |
| ISBN |
9781803248752 electronic book |
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1803248750 electronic book |
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