Deep Learning and Federated Architectures for Network Slicing Author

Deep Learning and Federated Architectures for Network Slicing examines artificial intelligence, distributed learning, and modern communication networks. It focuses on deep learning and federated learning architectures for network slicing, a framework that enables logically isolated and adaptable network environments. The discussion covers learning-based approaches to resource management, slice orchestration, traffic analysis, and service-aware optimization. It also considers distributed model training, data privacy, communication efficiency, and coordination across networked devices. The book connects machine learning with wireless communications, software-defined networking, cloud and edge computing, and network virtualization. By bringing these areas together, it provides a focused technical perspective on distributed learning for network slicing. The material is suitable for engineers, researchers, graduate-level readers, and professionals studying intelligent network systems today.