\u003cp\u003e\u003cb\u003eComprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications\u003c/b\u003e \u003cp\u003e\u003ci\u003eModel Optimization Methods for Efficient and Edge AI\u003c/i\u003e 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. \u003cp\u003eThe first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). \u003cp\u003eOther topics covered include: \u003cul\u003e\u003cli\u003eBuilding AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problems\u003c/li\u003e\u003cli\u003eGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers\u003c/li\u003e\u003cli\u003eCompressing AI models so that computational, memory, storage, and network requirements can be substantially reduced\u003c/li\u003e\u003cli\u003eAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data\u003c/li\u003e\u003cli\u003eOvercoming cyberattacks on mission-critical software systems by leveraging federated learning\u003c/li\u003e\u003c/ul\u003e \u003cp\u003eWritten in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, \u003ci\u003eModel Optimization Methods for Efficient and Edge AI\u003c/i\u003e is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.
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