Collaborative Learning for 6g Mobile Wireless Networks
Language: English
Published by Elsevier Science Jun 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Neuware - Collaborative Learning for 6G Mobile Wireless Networks gives a comprehensive introduction to the topic and its potential role in the development of 6G by explaining principles and presenting methods, algorithms, and uses cases. To achieve 6G s vision of intelligent and autonomous networks capable of self-optimization, self-healing, and context-aware adaptation, there is a need to develop advanced algorithms and frameworks to enable network elements to perceive, reason, and act autonomously in dynamic and unpredictable environments. However, traditional machine learning methods rely on centralized data collection and processing, making it a limitation for large-scale applications.Collaborative learning, as an emerging distributed approach, offers a powerful framework for harnessing the collective intelligence of distributed data sources while addressing key challenges such as privacy and security.
Seller Inventory # 9780443405709
- Title
- Collaborative Learning for 6g Mobile Wireless Networks
- Author
- Houda Hafi
- Publisher
- Elsevier Science Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0443405700
- ISBN 13
- 9780443405709
- Item weight
- 576 grams
- Dimensions
- 236x191x15 mm
Collaborative learning, as an emerging distributed approach, offers a powerful framework for harnessing the collective intelligence of distributed data sources while addressing key challenges such as privacy and security.
- Presents state-of-the-art, collaborative learning algorithms, including their principles, advantages, and disadvantages
- Shows how collaborative learning algorithms can overcome the drawbacks of traditional machine learning algorithms in the context of 6G networks
- Provides insights into how collaborative learning can enhance the capabilities of 6G networks technical aspects such as resource management, security and privacy, etc.
- Includes practical use cases where collaborative learning enhances the capabilities of 6G network real-world applications
- Looks into future trends and potential advances of collaborative learning for 6G
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Bouziane Brik, received his Engineer degree (Ranked First) in computer science, MSc degree and Ph.D from Laghouat University, Algeria. He is currently working as Assistant Professor in Computer Science ,department of Computing and Informatics College, at Sharjah university, UAE. He has worked as assistant professor at DRIVE department of Bourgogne university in France as well as a post-doc at university of Troyes, CESI school, and Eurecom research institute, in France. He researches resources management and security challenges of 5G network slicing in the context of H2020 European projects including MonB5G, 5GDrones, InDiD, and 5G-INSIGHT. His research interests also include 5G and Beyond networks, Explainable AI, and machine/deep learning for wireless networks.
Dr. Zakaria Abou El Houda received the Ph.D. degree in computer science from the University of Montreal, Montreal, QC, Canada, and the Ph.D. degree in computer engineering from the University of Technology of Troyes, Troyes, France. He is currently a professor with the Energy, Materials, and Telecommunications Center of the National Institute of Scientific Research (INRS), Canada. I am also a member of the INRS-UQO Joint Research Unit in Cybersecurity. Prior to joining INRS, He served as a research scientist in various institutions, contributing to significant research projects on the application of machine learning for intrusion detection systems, and studying the explainability and robustness of these systems. His current research interests include applied AI for intrusion detection systems, security in distributed/federated machine learning, and Blockchain for network security.
"About the title" may belong to another edition of this title.
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