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Federated Learning: Principles, Paradigms, and Applications
This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data.

The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well.

The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems.

The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included.

This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.

1145315414
Federated Learning: Principles, Paradigms, and Applications
This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data.

The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well.

The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems.

The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included.

This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.

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Federated Learning: Principles, Paradigms, and Applications

Federated Learning: Principles, Paradigms, and Applications

Federated Learning: Principles, Paradigms, and Applications

Federated Learning: Principles, Paradigms, and Applications

Overview

This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data.

The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well.

The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems.

The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included.

This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.

Product Details

ISBN-13: 9781774916384
Publisher: Apple Academic Press
Publication date: 09/20/2024
Pages: 352
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Jayakrushna Sahoo, PhD, has been associated with the Indian Institute of Information Technology, Kottayam, where he also serves as the Head of Computer Science and Engineering department. Before this, he worked with BML Munjal University, Gurgaon, India, as an Assistant Professor in the Department of Computer Science and Engineering. Dr. Sahoo has also worked as an ad hoc faculty in the Department of Computer Applications, National Institute of Technology, Jamshedpur, India. His publications have appeared in many reputed journals over the years. His research interests include data mining, machine learning, and federated learning. With his vast experience in research, he has been guiding several PhD scholars and has been associated with some of the country’s premier institutions. He has also worked in the capacity of resource person and technical panel member and has headed several international conferences in India. He earned his PhD with specialization in Data Mining from the Indian Institute of Technology in Kharagpur, India, and was awarded his MTech degree in Computer Science and Engineering from the International Institute of Information Technology, Bhubaneswar, India.

Mariya Ouaissa, PhD, is currently a Professor at an institute specializing in new information and communication technologies. She is also a research associate and practitioner with industry and academic experience. She earned her PhD in 2019 in Computer Science and Networks at the Laboratory of Modelisation of Mathematics and Computer Science at ENSAM-Moulay Ismail University, Meknes, Morocco. She is a networks and telecoms engineer, graduated in 2013 from the National School of Applied Sciences Khouribga, Morocco. She is a Co-Founder and IT Consultant at the IT Support and Consulting Center. She was working for the School of Technology of Meknes, Morocco, as a Visiting Professor from 2013 to 2021. She is member of the International Association of Engineers and International Association of Online Engineering, and since 2021, she is an ACM Professional Member. She is an expert reviewer with the Academic Exchange Information Centre (AEIC) and a brand ambassador with Bentham Science. She has served and continues to serve on technical programs and organizing committees of several conferences, symposiums, workshops, and conferences. She is also a reviewer for numerous international journals. Dr. Ouaissa has made contributions in the fields of information security and privacy, Internet of Things security, and wireless and constrained networks security. Her main research topics are IoT, M2M, D2D, WSN, cellular networks, and vehicular networks. She has published over 40 papers (book chapters, international journals, and conferences/workshops), 10 edited books, and eight specials issues as guest editor.

Akarsh K. Nair is a Doctoral Researcher at the Indian Institute of Information Technology, Kottayam, India, with a specialization in distributed learning, machine learning, federated learning, and edge intelligence. Mr. Nair has worked as an Assistant Professor in the Department of Computer Science at TEC College, Palakkad, India. He is also associated with iHub HCI Foundation of IIT, Himachal Pradesh, India, as a doctoral fellow. He has published several research articles in reputed scientific journals and international platforms. He has also acted as a reviewer for many prestigious scientific journals.

Table of Contents

1. The Evolution of Machine Learning: From Centralized to Distributed 2. Types of Federated Learning and Aggregation Techniques 3. Federated Learning for IoT/Edge/Fog Computing Systems 4. Adopting Federated Learning for Software-Defined Networks 5. Federated Learning in the Internet of Medical Things 6. Federated Learning Approaches for Intrusion Detection Systems: An Overview 7. Exploring Communication Efficient Strategies in Federated Learning Systems 8. Federated Learning and Privacy, Challenges, Threat and Attack Models, and Analysis 9. Analyzing Federated Learning from a Security Perspective 10. Blockchain Integrated Federated Learning in Edge/Fog/Cloud Systems for IoT-Based Healthcare Applications: A Survey 11. Incentive Mechanism for Federated Learning 12. Protected Shot-Based Federated Learning for Facial Expression Recognition

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