{"product_id":"model-optimization-methods-for-efficient-and-edge-ai-isbn-9781394219216","title":"Model Optimization Methods for Efficient and Edge AI","description":"\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 \u003c\/p\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. \u003c\/p\u003e\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). \u003c\/p\u003e\u003cp\u003eOther topics covered include: \u003c\/p\u003e\u003cul\u003e\n\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\n\u003cli\u003eGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers\u003c\/li\u003e\n\u003cli\u003eCompressing AI models so that computational, memory, storage, and network requirements can be substantially reduced\u003c\/li\u003e\n\u003cli\u003eAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data\u003c\/li\u003e\n\u003cli\u003eOvercoming cyberattacks on mission-critical software systems by leveraging federated learning\u003c\/li\u003e\n\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. \u003c\/p\u003e\u003cp\u003eAbout the Editors xxi\u003c\/p\u003e \u003cp\u003eList of Contributors xxiii\u003c\/p\u003e \u003cp\u003e1 Fundamentals of Edge AI and Federated Learning 1\u003cbr\u003e \u003ci\u003eAtefeh Hemmati, Hanieh Mohammadi Arzanagh, and Amir Masoud Rahmani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 AI Applications – Computer Vision and Natural Language Processing 25\u003cbr\u003e \u003ci\u003eBalakrishnan Chinnaiyan, Sundaravadivazhagan Balasubaramanian, Mahalakshmi Jeyabalu, and Gayathry S. Warrier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 An Overview of AI Platforms, Frameworks, Libraries, and Processors 43\u003cbr\u003e \u003ci\u003ePavan Kumar Akkisetty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Model Optimization Techniques for Edge Devices 57\u003cbr\u003e \u003ci\u003eYamini Nimmagadda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5 AI Model Optimization Techniques 87\u003cbr\u003e \u003ci\u003eG. Victor Daniel, M. Trupthi, G. Sridhar Reddy, A. Mallikarjuna Reddy, and K. Hemanth Sai\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Federated Learning: Introduction, Evolution, Working, Advantages, and Its Application in Various Domains 109\u003cbr\u003e \u003ci\u003eManoj Kumar Pandey, Naresh Kumar Kar, and Priyanka Gupta\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Application Domains of Federated Learning 127\u003cbr\u003e \u003ci\u003eS. Annamalai, N. Sangeetha, M. Kumaresan, Dommaraju Tejavarma, Gandhodi Harsha Vardhan, and A. Suresh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Advanced Architectures and Innovative Platforms for Federated Learning: A Comprehensive Exploration 145\u003cbr\u003e \u003ci\u003eNeha Bhati and Narayan Vyas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Federated Learning: Bridging Data Privacy and AI Advancements 157\u003cbr\u003e \u003ci\u003eD. Sumathi, Likitha Chowdary Botta, Mure Sai Jaideep Reddy, and Avi Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Securing Edge Learning: The Convergence of Block Chain and Edge Intelligence 169\u003cbr\u003e \u003ci\u003eRakhi Mutha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11 Training on Edge 197\u003cbr\u003e \u003ci\u003eYamini Nimmagadda\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 Architectural Patterns for the Design of Federated Learning Systems 223\u003cbr\u003e \u003ci\u003eVijay Anand Rajasekaran, Jayalakshmi Periyasamy, Madala Guru Brahmam, and Balamurugan Baluswamy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Federated Learning for Intelligent IoT Systems: Background, Frameworks, and Optimization Techniques 241\u003cbr\u003e \u003ci\u003ePartha Pratim Ray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Enhancing Cybersecurity Through Federated Learning: A Critical Evaluation of Strategies and Implications 281\u003cbr\u003e \u003ci\u003eM. Ashok Kumar, Aliyu Mohammed, S. Sumanth, and V. Sivanantham\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15 Blockchain for Securing Federated Learning Systems: Enhancing Privacy and Trust 299\u003cbr\u003e \u003ci\u003eTarun Kumar Vashishth, Vikas Sharma, Bhupendra Kumar, Kewal Krishan Sharma, Sachin Chaudhary, and Rajneesh Panwar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 Blockchain-Enabled Secure Federated Learning Systems for Advancing Privacy and Trust in Decentralized AI 321\u003cbr\u003e \u003ci\u003ePawan Whig, Rattan Sharma, Nikhitha Yathiraju, Anupriya Jain, and Seema Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 An Edge Artificial Intelligence Federated Recommender System for Virtual Classrooms 341\u003cbr\u003e \u003ci\u003eM. Sirish Kumar, T. Rupa Rani, U. Rakesh, Dyavarashetty Sunitha, and G. Sunil Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 Federated Learning in Smart Cities 351\u003cbr\u003e \u003ci\u003eSeyedeh Yasaman Hosseini Mirmahaleh and Amir Masoud Rahmani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIndex 391\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePethuru Raj Chelliah, PhD,\u003c\/b\u003e is the Chief Architect of the Edge AI division of Reliance Jio Platforms Ltd. (JPL), Bangalore, India. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eAmir Masoud Rahmani, PhD,\u003c\/b\u003e is an artificial intelligence faculty member at the National Yunlin University of Science and Technology, Taiwan. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eRobert Colby\u003c\/b\u003e is a Principal Engineer in IT Infrastructure responsible for Manufacturing Network Architecture and IoT Infrastructure at Intel Corporation. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eGayathri Nagasubramanian, PhD,\u003c\/b\u003e is an Assistant Professor with the Department of Computer Science and Engineering at GITAM University in Bengaluru, India. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eSunku Ranganath\u003c\/b\u003e is a Principal Product Manager for Edge Infrastructure Services at Equinix.   \u003c\/p\u003e\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 \u003c\/p\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. \u003c\/p\u003e\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). \u003c\/p\u003e\u003cp\u003eOther topics covered include: \u003c\/p\u003e\u003cul\u003e\n\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\n\u003cli\u003eGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablers\u003c\/li\u003e\n\u003cli\u003eCompressing AI models so that computational, memory, storage, and network requirements can be substantially reduced\u003c\/li\u003e\n\u003cli\u003eAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous data\u003c\/li\u003e\n\u003cli\u003eOvercoming cyberattacks on mission-critical software systems by leveraging federated learning\u003c\/li\u003e\n\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.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989635055845,"sku":"NP9781394219216","price":150.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781394219216.jpg?v=1761784899","url":"https:\/\/k12savings.com\/products\/model-optimization-methods-for-efficient-and-edge-ai-isbn-9781394219216","provider":"K12savings","version":"1.0","type":"link"}