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, IoT, and edge computing.
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, IoT, and edge computing.
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.
Inhaltsangabe
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. Rajalakshimi, M. Kavitha, and Rama Ranjini
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. Rajalakshimi, M. Kavitha, and Rama Ranjini
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