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Split Federated Learning for Secure IoT Applications

Split Federated Learning for Secure IoT Applications

Concepts, frameworks, applications and case studies  

Edited by Gururaj Harinahalli Lokesh, Geetabai S. Hukkeri, NZ Jhanjhi, Hong Lin

New approaches in federated learning and split learning have the potential to significantly improve ubiquitous intelligence in internet of things (IoT) applications. In split federated learning, the machine learning model is divided into smaller network segments, with each segment trained independently on a server using distributed local client data.

The split learning method mitigates two fundamental drawbacks of federated learning: affordability, and privacy and security. When running machine learning computation on devices with limited resources, assigning only a portion of the network to train at the client-side minimizes the processing burden, compared to running a complete network as in federated learning. In addition, neither client nor server has full access to the other, which is more secure.

This book reviews cutting edge technologies and advanced research in split federated learning. Coverage includes approaches to realizing and evaluating the effectiveness and advantages of federated learning and split-fed learning, the role of this technology in advancing and securing IoTs, advanced research on emerging AI models for preserving the privacy of the data owned by the clients, and the analysis and development of AI mechanisms in IoT architectures and applications. The use of split federated learning in natural language processing, recommendation systems, healthcare systems, emotion detection, smart agriculture, smart transportation and smart cities is discussed.

Split Federated Learning for Secure IoT Applications: Concepts, frameworks, applications and case studies offers useful insights to the latest developments in the field for researchers, engineers and scientists in academia and industry, who are working in computing, AI, data science and cybersecurity with a focus on federated learning, machine learning and deep learning.

About the Editors

Gururaj Harinahalli Lokesh is an associate professor in the Department of Information Technology at the Manipal Institute of Technology, Bengaluru, India. He is an editorial board member of the International Journal of Blockchains and Cryptocurrencies. He has published over 110 research papers in international journals and presented 20 papers at international conferences. He is a senior member of the IEEE, lifetime member of ISTE and CSI.

Geetabai S. Hukkeri is an assistant professor in the Department of Computer Science and Engineering at the Manipal Institute of Technology, Bengaluru, India. Her research interests include artificial intelligence, deep learning, machine learning, big data, computer vision and multimedia information retrieval. She is a member of ACM. She holds a PhD degree in Computer Science and Engineering from Visveswaraya Technological University, Belagavi, India.

NZ Jhanjhi is a professor at the School of Computer Science at Taylor's University, Malaysia, and is the program director for Postgraduate Research Degree Programmes in Computer Science. Additionally, he directs the Center for Smart Society (CSS5), playing a crucial role in advancing research and academic excellence at the university. He is a distinguished academician, researcher, and scientist in computer science, specializing in cybersecurity.

Hong Lin is a professor in Computer Science at the University of Houston, Downtown, TX, USA, where he has established the Grid Computing Lab through an NSF MRI grant. He previously worked on large-scale computational biology at Purdue University and network security at Nokia, Inc. He has published over 100 research papers in international journals and conferences. He is a senior member of the ACM.



Item Subjects:
Security

Publication Year: 2024

Pages: 350

ISBN-13: 978-1-83953-945-9

Format: HBK

Editors: Gururaj Harinahalli Lokesh, Geetabai S. Hukkeri, NZ Jhanjhi, Hong Lin

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