Energy Optimization and Security in Federated Learning for IoT Environments

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.

• Chapter 1: An overview of federated learning: empowering decentralized intelligenceChapter 2: Energy-efficient federated learning algorithmsChapter 3: Federated learning frameworks and algorithms for energy-efficient IoTChapter 4: Communication efficiency in federated learning in IoT environmentChapter 5: Energy-efficient federated learning methods for IoT environmentChapter 6: Energy optimization for IoT communicationChapter 7: Energy harvesting and energy-efficient communication protocols in IoTChapter 8: Energy consumption and efficiency in federated learning (FL) for IoTChapter 9: Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environmentsChapter 10: Hybrid security for IoT networks: from traditional security solutions to AI securityChapter 11: Secure data protection in federated learning for IoTChapter 12: Case studies and application for energy-efficient federated learning in IoTChapter 13: Energy-efficient federated learningChapter 14: Challenges future trends and research direction in FL methodsConclusion