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.

• 1. Introduction to Federated LearningVaneeza Mobin 2. Foundations of Deep LearningSajid Ullah 3. Chronicles of Deep LearningSyed Atif Ali Shah and Nasir Algeelani 4. User Participation and Incentives in Federated LearningMuhammad Ali Zeb and Samina Amin 5. A Hybrid Recommender System for MOOC Integrating Collaborative and Content-based FilteringSamina Amin and Muhammad Ali Zeb 6. Federated Learning in HealthcareMuhammad Hamza 7. Scalability and Efficiency in Federated LearningAlyan Zaib 8. Privacy Preservation in Federated LearningP. Keerthana, M. Kavitha, and Jayasudha Subburaj 9. Federated Learning: Trust, Fairness, and AccountabilitySana Daud 10. Federated Optimization AlgorithmsS. Biruntha, S. Rajalakshmi, M. Kavitha, and Rama Ranjini

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.

• 1. Introduction to Federated LearningVaneeza Mobin 2. Foundations of Deep LearningSajid Ullah 3. Chronicles of Deep LearningSyed Atif Ali Shah and Nasir Algeelani 4. User Participation and Incentives in Federated LearningMuhammad Ali Zeb and Samina Amin 5. A Hybrid Recommender System for MOOC Integrating Collaborative and Content-based FilteringSamina Amin and Muhammad Ali Zeb 6. Federated Learning in HealthcareMuhammad Hamza 7. Scalability and Efficiency in Federated LearningAlyan Zaib 8. Privacy Preservation in Federated LearningP. Keerthana, M. Kavitha, and Jayasudha Subburaj 9. Federated Learning: Trust, Fairness, and AccountabilitySana Daud 10. Federated Optimization AlgorithmsS. Biruntha, S. Rajalakshmi, M. Kavitha, and Rama Ranjini

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.

• 1. Introduction to Federated LearningVaneeza Mobin 2. Foundations of Deep LearningSajid Ullah 3. Chronicles of Deep LearningSyed Atif Ali Shah and Nasir Algeelani 4. User Participation and Incentives in Federated LearningMuhammad Ali Zeb and Samina Amin 5. A Hybrid Recommender System for MOOC Integrating Collaborative and Content-based FilteringSamina Amin and Muhammad Ali Zeb 6. Federated Learning in HealthcareMuhammad Hamza 7. Scalability and Efficiency in Federated LearningAlyan Zaib 8. Privacy Preservation in Federated LearningP. Keerthana, M. Kavitha, and Jayasudha Subburaj 9. Federated Learning: Trust, Fairness, and AccountabilitySana Daud 10. Federated Optimization AlgorithmsS. Biruntha, S. Rajalakshmi, M. Kavitha, and Rama Ranjini