Model Optimization Methods for Efficient and Edge AI

Federated Learning Architectures, Frameworks and Applications

Inbunden, Engelska, 2024

Av Pethuru Raj Chelliah, Pethuru Raj Chelliah, Amir Masoud Rahmani, Robert Colby, Gayathri Nagasubramanian, Sunku Ranganath, India) Chelliah, Pethuru Raj (Reliance Jio Platforms Ltd., Bangalore, Taiwan) Rahmani, Amir Masoud (National Yunlin University of Science and Technology, Robert (Intel Corporation) Colby, India) Nagasubramanian, Gayathri (GITAM University, Bengaluru, Sunku (Edge Infrastructure Services at Equinix) Ranganath

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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. The 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). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problemsGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablersCompressing AI models so that computational, memory, storage, and network requirements can be substantially reducedAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous dataOvercoming cyberattacks on mission-critical software systems by leveraging federated learningWritten in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.

Produktinformation

Utgivningsdatum2024-11-12
Mått178 x 254 x 24 mm
Vikt1 093 g
FormatInbunden
SpråkEngelska
Antal sidor432
FörlagJohn Wiley & Sons Inc
ISBN9781394219216

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Pethuru Raj Chelliah, PhD, is the Chief Architect of the Edge AI division of Reliance Jio Platforms Ltd. (JPL), Bangalore, India. Amir Masoud Rahmani, PhD, is an artificial intelligence faculty member at the National Yunlin University of Science and Technology, Taiwan. Robert Colby is a Principal Engineer in IT Infrastructure responsible for Manufacturing Network Architecture and IoT Infrastructure at Intel Corporation. Gayathri Nagasubramanian, PhD, is an Assistant Professor with the Department of Computer Science and Engineering at GITAM University in Bengaluru, India. Sunku Ranganath is a Principal Product Manager for Edge Infrastructure Services at Equinix.

About the Editors xxiList of Contributors xxiii1 Fundamentals of Edge AI and Federated Learning 1Atefeh Hemmati, Hanieh Mohammadi Arzanagh, and Amir Masoud Rahmani2 AI Applications – Computer Vision and Natural Language Processing 25Balakrishnan Chinnaiyan, Sundaravadivazhagan Balasubaramanian, Mahalakshmi Jeyabalu, and Gayathry S. Warrier3 An Overview of AI Platforms, Frameworks, Libraries, and Processors 43Pavan Kumar Akkisetty4 Model Optimization Techniques for Edge Devices 57Yamini Nimmagadda5 AI Model Optimization Techniques 87G. Victor Daniel, M. Trupthi, G. Sridhar Reddy, A. Mallikarjuna Reddy, and K. Hemanth Sai6 Federated Learning: Introduction, Evolution, Working, Advantages, and Its Application in Various Domains 109Manoj Kumar Pandey, Naresh Kumar Kar, and Priyanka Gupta7 Application Domains of Federated Learning 127S. Annamalai, N. Sangeetha, M. Kumaresan, Dommaraju Tejavarma, Gandhodi Harsha Vardhan, and A. Suresh Kumar8 Advanced Architectures and Innovative Platforms for Federated Learning: A Comprehensive Exploration 145Neha Bhati and Narayan Vyas9 Federated Learning: Bridging Data Privacy and AI Advancements 157D. Sumathi, Likitha Chowdary Botta, Mure Sai Jaideep Reddy, and Avi Das10 Securing Edge Learning: The Convergence of Block Chain and Edge Intelligence 169Rakhi Mutha11 Training on Edge 197Yamini Nimmagadda12 Architectural Patterns for the Design of Federated Learning Systems 223Vijay Anand Rajasekaran, Jayalakshmi Periyasamy, Madala Guru Brahmam, and Balamurugan Baluswamy13 Federated Learning for Intelligent IoT Systems: Background, Frameworks, and Optimization Techniques 241Partha Pratim Ray14 Enhancing Cybersecurity Through Federated Learning: A Critical Evaluation of Strategies and Implications 281M. Ashok Kumar, Aliyu Mohammed, S. Sumanth, and V. Sivanantham15 Blockchain for Securing Federated Learning Systems: Enhancing Privacy and Trust 299Tarun Kumar Vashishth, Vikas Sharma, Bhupendra Kumar, Kewal Krishan Sharma, Sachin Chaudhary, and Rajneesh Panwar16 Blockchain-Enabled Secure Federated Learning Systems for Advancing Privacy and Trust in Decentralized AI 321Pawan Whig, Rattan Sharma, Nikhitha Yathiraju, Anupriya Jain, and Seema Sharma17 An Edge Artificial Intelligence Federated Recommender System for Virtual Classrooms 341M. Sirish Kumar, T. Rupa Rani, U. Rakesh, Dyavarashetty Sunitha, and G. Sunil Kumar18 Federated Learning in Smart Cities 351Seyedeh Yasaman Hosseini Mirmahaleh and Amir Masoud RahmaniIndex 391