Название: Model Optimization Methods for Efficient and Edge AI: Federated Learning Architectures, Frameworks and Applications
Автор: Pethuru Raj Chelliah, Amir Masoud Rahmani, Robert Colby, Gayathri Nagasubramanian, Sunku Ranganath
Издательство: Wiley-IEEE Press
Год: 2025
Страниц: 414
Язык: английский
Формат: pdf (true), epub
Размер: 29.4 MB
Comprehensive overview of the fledgling domain of Federated Learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications. Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of Federated Learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more.