Federated Learning with Python
Federated Learning with Python

Author: Kiyoshi Nakayama PhD

Publisher: Packt Publishing Ltd

Published: 2022-10-28

Total Pages: 327

ISBN-13: 1803248750

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Learn the essential skills for building an authentic federated learning system with Python and take your machine learning applications to the next level Key FeaturesDesign distributed systems that can be applied to real-world federated learning applications at scaleDiscover multiple aggregation schemes applicable to various ML settings and applicationsDevelop a federated learning system that can be tested in distributed machine learning settingsBook 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 learnDiscover the challenges related to centralized big data ML that we currently face along with their solutionsUnderstand the theoretical and conceptual basics of FLAcquire design and architecting skills to build an FL systemExplore the actual implementation of FL servers and clientsFind out how to integrate FL into your own ML applicationUnderstand various aggregation mechanisms for diverse ML scenariosDiscover popular use cases and future trends in FLWho 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.

Big Data over Networks
Big Data over Networks

Author: Shuguang Cui

Publisher: Cambridge University Press

Published: 2016-01-14

Total Pages: 459

ISBN-13: 1107099005

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Examines the crucial interaction between big data and communication, social and biological networks using critical mathematical tools and state-of-the-art research.

Federated Learning
Federated Learning

Author: Qiang Yang

Publisher: Springer Nature

Published: 2020-11-25

Total Pages: 291

ISBN-13: 3030630765

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This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”

Building Android Apps in Python Using Kivy with Android Studio
Building Android Apps in Python Using Kivy with Android Studio

Author: Ahmed Fawzy Mohamed Gad

Publisher: Apress

Published: 2019-10-14

Total Pages: 432

ISBN-13: 1484250311

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Start building Python-based Android applications using Kivy with Android Studio. Through in-depth examples, this book teaches you everything you need to create your first Android application in Python and publish on Google Play. Building Android Apps in Python Using Kivy with Android Studio takes you through the basics of Kivy by discussing its application structure, widgets, and event handling. The KV language is then introduced for separating the logic and GUI by adding widgets within a KV file. You will then learn how to utilize Android camera using Kivy, build the HTTP server using Flask, and create and manage multiple screens to help you design your own applications. Through detailed step-by-step instructions, you will create your first multi-level cross-platform game that includes animation and sound effects. Following this, the process of converting the Kivy application into an Android application using Buildozer and Python-4-Android is covered in detail. You will then learn how to edit the generated Android Studio project into Android Studio by adding extensions to the original application. The widgets added in Kivy could be handled within Android Studio. Moreover, Android views could be added to enrich the Kivy application. The resulting Android application created with Kivy can be hosted on Google Play to download and install as a regular Android application. At the end, this book will give you the basic knowledge of Kivy needed to build cross-platform Android applications, produce an Android Studio project, and understand how it all works in detail. What You Will Learn Build cross-platform applications from scratch using Kivy in detailCreate a cross-platform interactive multi-level game from the ground upExamine the pipeline of building an Android app from the Python Kivy appUnderstand the structure of the Android Studio project produced by KivyRecognize how to extend the application within Android Studio by adding more Android views to the application main activity. Who This Book Is For Python developers with no previous experience in Kivy who are looking to create their first Android application completely in Python.

Federated Learning
Federated Learning

Author: Qiang Qiang Yang

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 189

ISBN-13: 3031015851

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How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

Federated Learning Systems
Federated Learning Systems

Author: Muhammad Habib ur Rehman

Publisher: Springer Nature

Published: 2021-06-11

Total Pages: 207

ISBN-13: 3030706044

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This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.

Handbook on Federated Learning
Handbook on Federated Learning

Author: Saravanan Krishnan

Publisher: CRC Press

Published: 2024-01-09

Total Pages: 381

ISBN-13: 1003837522

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Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.

Federated Learning
Federated Learning

Author: Lam M. Nguyen

Publisher: Elsevier

Published: 2024-02-09

Total Pages: 436

ISBN-13: 0443190380

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Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II featuresemerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data.Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors. - Presents the fundamentals and a survey of key developments in the field of federated learning - Provides emerging, state-of-the art topics that build on fundamentals - Contains industry applications - Gives an overview of visions of the future

Federated Learning: From Algorithms To System Implementation
Federated Learning: From Algorithms To System Implementation

Author: Liefeng Bo

Publisher: World Scientific

Published: 2024-08-16

Total Pages: 546

ISBN-13: 9811292566

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Authored by researchers and practitioners who build cutting-edge federated learning applications to solve real-world problems, this book covers the spectrum of federated learning technology from concepts and application scenarios to advanced algorithms and finally system implementation in three parts. It provides a comprehensive review and summary of federated learning technology, as well as presenting numerous novel federated learning algorithms which no other books have summarized. The work also references the most recent papers, articles and reviews from the past several years to keep pace with the academic and industrial state of the art of federated learning.The first part lays a foundational understanding of federated learning by going through its definition and characteristics, and also possible application scenarios and related privacy protection technologies. The second part elaborates on some of the federated learning algorithms innovated by JD Technology which encompass both vertical and horizontal scenarios, including vertical federated tree models, linear regression, kernel learning, asynchronous methods, deep learning, homomorphic encryption, and reinforcement learning. The third and final part shifts in scope to federated learning systems — namely JD Technology's own FedLearn system — by discussing its design and implementation using gRPC, in addition to specific performance optimization techniques plus integration with blockchain technology.This book will serve as a great reference for readers who are experienced in federated learning algorithms, building privacy-preserving machine learning applications or solving real-world problems with privacy-restricted scenarios.

Essential Federated Learning
Essential Federated Learning

Author: Robert Johnson

Publisher: HiTeX Press

Published: 2024-10-27

Total Pages: 249

ISBN-13:

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"Essential Federated Learning: AI at the Edge" offers a comprehensive exploration into the transformative domain of federated learning, an innovative approach reshaping the AI landscape by enabling data decentralization. This book demystifies the foundational concepts of federated learning, capturing its potential to increase privacy, enhance data security, and empower industries across sectors such as healthcare, finance, and beyond. By keeping data localized, federated learning minimizes privacy concerns while leveraging the power and capability of edge computing. Each chapter meticulously builds upon the last, guiding readers from basic principles to advanced applications, providing a balanced understanding of technical architectures, algorithms, and real-world implementations. Rich with insights into the ethical and social implications of federated learning, this book addresses the pressing challenges and future directions that are critical for its evolution. Topics such as privacy preservation, bias mitigation, and regulatory compliance are thoroughly examined, offering a holistic view of how federated learning can be applied responsibly and effectively. Whether you're a researcher, practitioner, or policy-maker, "Essential Federated Learning: AI at the Edge" offers the essential knowledge needed to harness the advantages of this cutting-edge technology, ensuring readers are well-equipped to navigate the rapidly expanding landscape of AI and edge computing.