Federated learning with Python : design and implement a federated learning system and develop applications using existing frameworks / George Jeno.

Format:

Book

Author/Creator:

Jeno, George, author.

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Physical Description:

1 online resource

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2022]

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

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

Security for FL systems

Decentralized FL and blockchain

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

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

Part 2 The Design and Implementation of the Federated Learning System

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

Notes:

Includes bibliographical references and index.

OCLC-licensed vendor bibliographic record.

Description based on print version record.

ISBN:

9781803248752

1803248750

OCLC:

1349274137