Federated Learning

Unlocking the Power of Collaborative Intelligence
1. Auflage 2024
ISBN: 978-1-032-72432-4
Verlag: CRC Press

Buch, Englisch, 194 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 461 g

Reihe: Chapman & Hall/CRC Artificial Intelligence and Robotics Series

Federated Learning: Unlocking the Power of Collaborative Intelligence is a definitive guide to the transformative potential of federated learning. This book delves into federated learning principles, techniques, and applications, and offers practical insights and real-world case studies to showcase its capabilities and benefits.

The book begins with a survey of the fundamentals of federated learning and its significance in the era of privacy concerns and data decentralization. Through clear explanations and illustrative examples, the book presents various federated learning frameworks, architectures, and communication protocols. Privacy-preserving mechanisms are also explored, such as differential privacy and secure aggregation, offering the practical knowledge needed to address privacy challenges in federated learning systems. This book concludes by highlighting the challenges and emerging trends in federated learning, emphasizing the importance of trust, fairness, and accountability, and provides insights into scalability and efficiency considerations.

With detailed case studies and step-by-step implementation guides, this book shows how to build and deploy federated learning systems in real-world scenarios – such as in healthcare, finance, Internet of things (IoT), and edge computing. Whether you are a researcher, a data scientist, or a professional exploring the potential of federated learning, this book will empower you with the knowledge and practical tools needed to unlock the power of federated learning and harness the collaborative intelligence of distributed systems.

Key Features:

- Provides a comprehensive guide on tools and techniques of federated learning

- Highlights many practical real-world examples

- Includes easy-to-understand explanations

Zielgruppe

Postgraduate and Professional Reference

Autoren/Hrsg.

Uddin, M. Irfan

Mashwani, Wali Khan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

1. Introduction to Federated Learning

Vaneeza Mobin 

2. Foundations of Deep Learning

Sajid Ullah 

3. Chronicles of Deep Learning

Syed Atif Ali Shah and Nasir Algeelani 

4. User Participation and Incentives in Federated Learning

Muhammad Ali Zeb and Samina Amin 

5. A Hybrid Recommender System for MOOC Integrating Collaborative and Content-based Filtering

Samina Amin and Muhammad Ali Zeb 

6. Federated Learning in Healthcare

Muhammad Hamza 

7. Scalability and Efficiency in Federated Learning

Alyan Zaib 

8. Privacy Preservation in Federated Learning

P. Keerthana, M. Kavitha, and Jayasudha Subburaj 

9.  Federated Learning: Trust, Fairness, and Accountability

Sana Daud 

10.  Federated Optimization Algorithms

S. Biruntha, S. Rajalakshmi, M. Kavitha, and Rama Ranjini

Über Autor(innen)

M. Irfan Uddin is currently working as a faculty member at the Institute of Computing, Kohat University of Science and Technology, Kohat, Pakistan. He has received his academic qualifications in computer science and has worked as a researcher on funded projects. He is involved in teaching and research activities related to different diverse computer science topics and has more than 18 years of teaching plus research experience. He is a member of IEEE, ACM, and HiPEAC. He has organized national and international seminars, workshops, and conferences. He has published over a hundred research papers in international journals and conferences. His research interests include machine learning, data science, artificial neural networks, deep learning, convolutional neural networks, recurrent neural networks, attention models, reinforcement learning, generative adversarial networks, computer vision, image processing, machine translation, natural language processing, speech recognition, big data analytics, parallel programming, multi-core, many-core, and GPUs.

Wali Khan Mashwani received an M.Sc. degree in mathematics from the University of Peshawar, Khyber Pakhtunkhwa, Pakistan, in 1996, and a Ph.D. degree in mathematics from the University of Essex, UK, in 2012. He is currently a Professor of Mathematics and the Director of the Institute of Numerical Sciences, Kohat University of Science and Technology (KUST), Khyber Pakhtunkhwa. He is also a Dean of the Physical and Numerical Science faculty at KUST. He has published more than 100 academic papers in peer-reviewed international journals and conference proceedings. His research interests include evolutionary computation, hybrid evolutionary multi-objective algorithms, decomposition-based evolutionary methods for multi-objective optimization, mathematical programming, numerical analysis, and artificial neural networks.