Energy Optimization and Security in Federated Learning for IoT Environments
Published on 4. February 2025
Book
Hardback
349 pages
978-1-83953-962-6 (ISBN)
Description
Smart environments such as smart homes and industrial automation have been transformed by the rapid developments in internet of things (IoT) devices and systems. However, the widespread use of these devices poses significant difficulties, particularly in settings with limited energy resources. Due to the significant energy consumption and communication overhead associated with delivering huge amounts of data, traditional machine learning algorithms which rely on centralized cloud servers for training are not always suitable.
Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems.
In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure.
Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, R&D professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.
Federated learning is a decentralized strategy that enables collaborative machine learning model training while keeping the data local on edge devices. It has emerged as a suitable solution to overcome the energy constraints of IoT devices. Federated learning works by dividing the training process among several nodes and using the processing power of edge devices. As opposed to sending raw data to a central server, only the model changes are communicated thereby considerably lowering the communication costs while protecting data privacy. This strategy reduces energy usage while simultaneously reducing network latency and bandwidth-related problems.
In this book, the authors show how to optimise federated learning algorithms and develop new communication protocols and resource allocation methodologies to maximize energy savings while retaining respectable model accuracy, to develop long-lasting and scalable IoT solutions that can function independently with no dependency on an external cloud infrastructure.
Energy Optimization and Security in Federated Learning for IoT Environments is intended to be a useful resource for academic researchers, R&D professionals, IoT engineers in the IT industry, and data scientists creating optimised AI models to be run in cloud environments.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 21 mm
Weight
671 gr
ISBN-13
978-1-83953-962-6 (9781839539626)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Balamurugan Balusamy is the associate dean of student engagement at Shiv Nadar University, Delhi-NCR, India. His research focuses on cybersecurity, blockchain, data science, cloud environments, IoT, and engineering education. He has published over 200 papers, contributed to 80 edited and authored books, and given 200 talks at international events and symposiums. He serves in the advisory committee for several start-ups and forums and does consultancy work for the industry on Industrial IoT. He holds a PhD degree in 'Investigations of Cloud Computing Access Control Techniques' from VIT University, Vellore, India.
Daniel Arockiam is an associate professor in the Department of Computer Science and Engineering at Amity University, Gwalior, Madhya Pradesh, India. His research areas include neural networks, AI, ML, DL, FL, and cloud computing. He has published 45 papers in international journals and conferences. He is a member of CSTA, IFERP, and IAEng. He was a member of the organising committee for GuCon 2019 and 2020 conferences. He holds a PhD degree in 'Hybrid Approaches to Optimise the Performance of Load Balancing and Task Scheduling in Cloud Environment' from Shri Venkateshwara University, India.
Pethuru Raj is a chief architect and the vice president of Reliance Jio Platforms Ltd. Bangalore, India. He focuses on the Internet of Things (IoT); artificial intelligence; big data analytics, blockchain, digital twins; edge, fog, and cloud computing; reliability engineering; microservices architecture (MSA), 5G and 6G, and event-driven architecture (EDA). He previously worked at IBM global Cloud Center of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). He has authored and edited 35+ books and contributed to 35 book chapters. He is a member of ACM. He holds a PhD degree on 'Formal Language Theory and Finite Automata' from Anna University, Chennai, India.
Daniel Arockiam is an associate professor in the Department of Computer Science and Engineering at Amity University, Gwalior, Madhya Pradesh, India. His research areas include neural networks, AI, ML, DL, FL, and cloud computing. He has published 45 papers in international journals and conferences. He is a member of CSTA, IFERP, and IAEng. He was a member of the organising committee for GuCon 2019 and 2020 conferences. He holds a PhD degree in 'Hybrid Approaches to Optimise the Performance of Load Balancing and Task Scheduling in Cloud Environment' from Shri Venkateshwara University, India.
Pethuru Raj is a chief architect and the vice president of Reliance Jio Platforms Ltd. Bangalore, India. He focuses on the Internet of Things (IoT); artificial intelligence; big data analytics, blockchain, digital twins; edge, fog, and cloud computing; reliability engineering; microservices architecture (MSA), 5G and 6G, and event-driven architecture (EDA). He previously worked at IBM global Cloud Center of Excellence (CoE), Wipro Consulting Services (WCS), and Robert Bosch Corporate Research (CR). He has authored and edited 35+ books and contributed to 35 book chapters. He is a member of ACM. He holds a PhD degree on 'Formal Language Theory and Finite Automata' from Anna University, Chennai, India.
Content
Chapter 1: An overview of federated learning: empowering decentralized intelligence
Chapter 2: Energy-efficient federated learning algorithms
Chapter 3: Federated learning frameworks and algorithms for energy-efficient IoT
Chapter 4: Communication efficiency in federated learning in IoT environment
Chapter 5: Energy-efficient federated learning methods for IoT environment
Chapter 6: Energy optimization for IoT communication
Chapter 7: Energy harvesting and energy-efficient communication protocols in IoT
Chapter 8: Energy consumption and efficiency in federated learning (FL) for IoT
Chapter 9: Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments
Chapter 10: Hybrid security for IoT networks: from traditional security solutions to AI security
Chapter 11: Secure data protection in federated learning for IoT
Chapter 12: Case studies and application for energy-efficient federated learning in IoT
Chapter 13: Energy-efficient federated learning
Chapter 14: Challenges future trends and research direction in FL methods
Conclusion
Chapter 2: Energy-efficient federated learning algorithms
Chapter 3: Federated learning frameworks and algorithms for energy-efficient IoT
Chapter 4: Communication efficiency in federated learning in IoT environment
Chapter 5: Energy-efficient federated learning methods for IoT environment
Chapter 6: Energy optimization for IoT communication
Chapter 7: Energy harvesting and energy-efficient communication protocols in IoT
Chapter 8: Energy consumption and efficiency in federated learning (FL) for IoT
Chapter 9: Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments
Chapter 10: Hybrid security for IoT networks: from traditional security solutions to AI security
Chapter 11: Secure data protection in federated learning for IoT
Chapter 12: Case studies and application for energy-efficient federated learning in IoT
Chapter 13: Energy-efficient federated learning
Chapter 14: Challenges future trends and research direction in FL methods
Conclusion