Green Machine Learning Protocols for Future Communication Networks

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data, which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications, efforts are required toward designing new machine learning protocols and modifying the existing ones, which consume less energy, i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols, this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.

Table of Contents:
1. Green Machine Learning for Cellular Networks by Saad Aslam, Houshyar Honar Pajooh, Muhammad Nadeem and Fakhrul Alam. 2. Green Machine Learning Protocols for Cellular Communication by Mamoon M. Saeed, Elmustafa Sayed Ali, Rashid A. Saeed and Mohammad Abdul Azim. 3. Green Federated Learning-based Models and Protocols by Afaf Taik, Amine Abouaomar and Soumaya Cherkaoui. 4. GREEN6G: Chameleon Federated Learning for Energy Efficient Network Slicing in Beyond 5G Systems by Anurag Thantharate. 5. Green Machine Learning Approaches for Cloud-Based Communications by Mona Bakri Hassan, Elmustafa Sayed Ali and Rashid A. Saeed. 6. Green Machine Learning for Internet of Things: Current Solutions and Future Challenges by Hajar Moudoud, Zoubeir Mlika, Soumaya Cherkaoui and Lyes Khoukhi. 7. Green Machine Learning Protocols for Machine-to-Machine Communication by Sreenivasa Reddy Yeduri, Sindhusha Jeeru and Linga Reddy Cenkeramaddi.