Federated Learning Over Wireless Edge Networks
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Federated Learning Over Wireless Edge Networks
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Author : Wei Yang Bryan Lim
language : en
Publisher: Springer Nature
Release Date : 2022-09-28
Federated Learning Over Wireless Edge Networks written by Wei Yang Bryan Lim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-28 with Technology & Engineering categories.
This book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively.
Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2020-11-25
Federated Learning written by Qiang Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-25 with Computers categories.
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
Edge Ai
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Author : Xiaofei Wang
language : en
Publisher: Springer Nature
Release Date : 2020-08-31
Edge Ai written by Xiaofei Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-31 with Computers categories.
As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.
Federated Learning For Wireless Networks
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Author : Choong Seon Hong
language : en
Publisher: Springer Nature
Release Date : 2022-01-01
Federated Learning For Wireless Networks written by Choong Seon Hong and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-01 with Computers categories.
Recently machine learning schemes have attained significant attention as key enablers for next-generation wireless systems. Currently, wireless systems are mostly using machine learning schemes that are based on centralizing the training and inference processes by migrating the end-devices data to a third party centralized location. However, these schemes lead to end-devices privacy leakage. To address these issues, one can use a distributed machine learning at network edge. In this context, federated learning (FL) is one of most important distributed learning algorithm, allowing devices to train a shared machine learning model while keeping data locally. However, applying FL in wireless networks and optimizing the performance involves a range of research topics. For example, in FL, training machine learning models require communication between wireless devices and edge servers via wireless links. Therefore, wireless impairments such as uncertainties among wireless channel states, interference, and noise significantly affect the performance of FL. On the other hand, federated-reinforcement learning leverages distributed computation power and data to solve complex optimization problems that arise in various use cases, such as interference alignment, resource management, clustering, and network control. Traditionally, FL makes the assumption that edge devices will unconditionally participate in the tasks when invited, which is not practical in reality due to the cost of model training. As such, building incentive mechanisms is indispensable for FL networks. This book provides a comprehensive overview of FL for wireless networks. It is divided into three main parts: The first part briefly discusses the fundamentals of FL for wireless networks, while the second part comprehensively examines the design and analysis of wireless FL, covering resource optimization, incentive mechanism, security and privacy. It also presents several solutions based on optimization theory, graph theory, and game theory to optimize the performance of federated learning in wireless networks. Lastly, the third part describes several applications of FL in wireless networks.
Federated Learning Systems
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Author : Muhammad Habib ur Rehman
language : en
Publisher: Springer Nature
Release Date : 2025-04-26
Federated Learning Systems written by Muhammad Habib ur Rehman and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-26 with Computers categories.
This book dives deep into both industry implementations and cutting-edge research driving the Federated Learning (FL) landscape forward. FL enables decentralized model training, preserves data privacy, and enhances security without relying on centralized datasets. Industry pioneers like NVIDIA have spearheaded the development of general-purpose FL platforms, revolutionizing how companies harness distributed data. Alternately, for medical AI, FL platforms, such as FedBioMed, enable collaborative model development across healthcare institutions to unlock massive value. Research advances in PETs highlight ongoing efforts to ensure that FL is robust, secure, and scalable. Looking ahead, federated learning could transform public health by enabling global collaboration on disease prevention while safeguarding individual privacy. From recommendation systems to cybersecurity applications, FL is poised to reshape multiple domains, driving a future where collaboration and privacy coexist seamlessly.
Federated Learning
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Author : Jayakrushna Sahoo
language : en
Publisher: CRC Press
Release Date : 2024-09-20
Federated Learning written by Jayakrushna Sahoo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-20 with Computers categories.
This new book provides an in-depth understanding of federated learning, a new and increasingly popular learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. The volume explores how federated learning integrates AI technologies, such as blockchain, machine learning, IoT, edge computing, and fog computing systems, allowing multiple collaborators to build a robust machine-learning model using a large dataset. It highlights the capabilities and benefits of federated learning, addressing critical issues such as data privacy, data security, data access rights, and access to heterogeneous data. The volume first introduces the general concepts of machine learning and then summarizes the federated learning system setup and its associated terminologies. It also presents a basic classification of FL, the application of FL for various distributed computing scenarios, an integrated view of applications of software-defined networks, etc. The book also explores the role of federated learning in the Internet of Medical Things systems as well. The book provides a pragmatic analysis of strategies for developing a communication-efficient federated learning system. It also details the applicability of blockchain with federated learning on IoT-based systems. It provides an in-depth study of FL-based intrusion detection systems, discussing their taxonomy and functioning and showcasing their superiority over existing systems. The book is unique in that it evaluates the privacy and security aspects in federated learning. The volume presents a comprehensive analysis of some of the common challenges, proven threats, and attack strategies affecting FL systems. Special coverage on protected shot-based federated learning for facial expression recognition is also included. This comprehensive book, Federated Learning: Principles, Paradigms, and Applications, will enable research scholars, information technology professionals, and distributed computing engineers to understand various aspects of federated learning concepts and computational techniques for real-life implementation.
Mobile Edge Artificial Intelligence
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Author : Yuanming Shi
language : en
Publisher: Academic Press
Release Date : 2021-08-07
Mobile Edge Artificial Intelligence written by Yuanming Shi and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-07 with Computers categories.
Mobile Edge Artificial Intelligence: Opportunities and Challenges presents recent advances in wireless technologies and nonconvex optimization techniques for designing efficient edge AI systems. The book includes comprehensive coverage on modeling, algorithm design and theoretical analysis. Through typical examples, the powerfulness of this set of systems and algorithms is demonstrated, along with their abilities to make low-latency, reliable and private intelligent decisions at network edge. With the availability of massive datasets, high performance computing platforms, sophisticated algorithms and software toolkits, AI has achieved remarkable success in many application domains. As such, intelligent wireless networks will be designed to leverage advanced wireless communications and mobile computing technologies to support AI-enabled applications at various edge mobile devices with limited communication, computation, hardware and energy resources. - Presents advanced key enabling techniques, including model compression, wireless MapReduce and wireless cooperative transmission - Provides advanced 6G wireless techniques, including over-the-air computation and reconfigurable intelligent surface - Includes principles for designing communication-efficient edge inference systems, communication-efficient training systems, and communication-efficient optimization algorithms for edge machine learning
Integrated Sensing And Communications For Future Wireless Networks
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Author : Aryan Kaushik
language : en
Publisher: Elsevier
Release Date : 2024-12-02
Integrated Sensing And Communications For Future Wireless Networks written by Aryan Kaushik and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-02 with Technology & Engineering categories.
Integrated Sensing and Communications for Future Wireless Networks: Principles, Advances and Key Enabling Technologies presents the principles, methods, and algorithms of ISAC, an overview of the essential enabling technologies, as well as the latest research and future directions. Suitable for academic researchers and post graduate students as well as industry R&D engineers, this book is the definitive reference in this interdisciplinary field that is being seen as a technology to enable emerging applications such as vehicular networks, environmental monitoring, remote sensing, IoT, smart cities. Importantly, ISAC has been identified as an enabling technology for B5G/6G, and the next-generation Wi-Fi system. ISAC brings together a range of technologies: radar sensing, reconfigurable intelligent surfaces, holographic surfaces through to high frequency terahertz, PHY security, channel signaling, multiple access, and machine learning. - Gives an overview of ISAC technology – its potential, the challenges and future research trajectory - Presents the future directions of ISAC - Includes discussion of the following technologies: i. Intelligent Metasurfaces for ISAC; ii. Machine Learning and AI for ISAC; iii. ISAC Waveform Design and Full-Duplex; iv. Millimeter Wave, Terahertz, and Beamforming for ISAC; v. Network Architectural Aspects of Integrating Sensing
Artificial Intelligence Applications And Innovations
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Author : Ilias Maglogiannis
language : en
Publisher: Springer Nature
Release Date : 2025-06-24
Artificial Intelligence Applications And Innovations written by Ilias Maglogiannis and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-24 with Computers categories.
This four-volume set constitutes the proceedings of the 21st IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2025, which was held in Limassol, Cyprus, during June 2025. The 123 full papers and 7 short papers were presented in this volume were carefully reviewed and selected from 303 submissions. They focus on ethical-moral AI aspects related to its Environmental impact, Privacy, Transparency, Bias, Discrimination and Fairness.
Distributed Machine Learning And Computing
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Author : M. Hadi Amini
language : en
Publisher: Springer Nature
Release Date : 2024-05-28
Distributed Machine Learning And Computing written by M. Hadi Amini and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-28 with Technology & Engineering categories.
This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering systems. The contributors explore how these techniques can be applied to different real-world problems. It is suitable for students and researchers interested in conducting research in multidisciplinary areas that rely on distributed machine learning and computing techniques.