Energy Optimization and Security in Federated Learning for IoT Environments / edited by Balamurugan Balusamy, Daniel Arockiam and Pethuru Raj.

Format:

Book

Contributor:

Balusamy, Balamurugan, editor.

Arockiam, Daniel, editor.

Raj, Pethuru, editor.

Series:

Computing and Networks Series

Language:

English

Subjects (All):

Internet of things--Energy conservation.

Internet of things.

Federated searching.

Machine learning.

Physical Description:

1 online resource (303 pages)

Edition:

1st ed.

Place of Publication:

London, United Kingdom : Institution of Engineering and Technology, 2024.

Summary:

This book covers optimised federated learning algorithms and new communication protocols and resource allocation methodologies, to maximize energy savings while retaining respectable model accuracy, and develop long-lasting and scalable IoT solutions that can function independently with dependency on an external cloud infrastructures.

Contents:

An overview of federated learning: empowering decentralized intelligence / Ghanshyam Prasad Dubey, Ayush Giri, Daniel Arockiam and V. Sathya Priya

Energy-efficient federated learning algorithms Amutha Prabakar Muniyandi, Daniel Arockiam, Feslin Anish Mon and N. Deepa

Federated learning frameworks and algorithms for energy-efficient IoT Kiran Malik, Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal and Rajani

Communication efficiency in federated learning in IoT environment Amutha Prabakar Muniyandi, L. Godlin Atlas, N. Deepa and Mahmoud Ahmad Al-Khasawneh

Energy-efficient federated learning methods for IoT environment Amutha Prabakar Muniyandi, Feslin Anish Mon, L. Godlin Atlas and Mahmoud Ahmad Al-Khasawneh

Energy optimization for IoT communication G. Arun Prasath, S. Dinesh Krishnan, A.S. Shanthi and Daniel Arockiam

Energy harvesting and energy-efficient communication protocols in IoT Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Kiran Malik, B. Tirapathi Reddy and Daniel Arockiam

Energy consumption and efficiency in federated learning (FL) for IoT Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Kiran Malik and K. Babu

Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments S. Tamizharasi, P. Rubini, S. Saravana Kumar and Daniel Arockiam

Hybrid security for IoT networks: from traditional security solutions to AI security Rajeev Goyal, Samta Jain Goyal, Madhavi Dhingra, Shyam Sunder Gupta and Daniel Arockiam

Secure data protection in federated learning for IoT Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Kiran Malik, Sumit Singh Dhanda and K. Babu

Case studies and application for energy-efficient federated learning in IoT M. Nalini, S. Jayasri, S. Nagammai and Daniel Arockiam

Energy-efficient federated learning Vijay Ramalingam, A. Arul Prakash, S. Vignesh, R. Rahin Batcha and D. Saravanan

Challenges future trends and research direction in FL methods D. Saravanan, Vijay Ramalingam, A. Arul Prakash, S. Vignesh and R. Rahin Batcha

Conclusions.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-83724-352-2

1-83953-963-1

OCLC:

1485003976