Handbook On Federated Learning
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Handbook On Federated Learning
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Author : Saravanan Krishnan
language : en
Publisher: CRC Press
Release Date : 2023-12-15
Handbook On Federated Learning written by Saravanan Krishnan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with Computers categories.
Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.
Handbook On Federated Learning
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Author : Saravanan Krishnan
language : en
Publisher: CRC Press
Release Date : 2024-01-09
Handbook On Federated Learning written by Saravanan Krishnan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.
Mobile, wearable, and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information, it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy, large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL, central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing, but the model processing happens at the sight of the server, thus taking too long. The model can be placed in the end-user device, but continuous learning is a challenge to overcome, as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects.
Handbook Of Trustworthy Federated Learning
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Author : My T. Thai
language : en
Publisher: Springer Nature
Release Date : 2024-09-03
Handbook Of Trustworthy Federated Learning written by My T. Thai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-03 with Computers categories.
This handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on federated learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various topics. The readership spans from researchers and academics to practitioners who are deeply engaged or are starting to venture into the realms of trustworthy federated learning. First introduced in 2016, federated learning allows devices to collaboratively learn a shared model while keeping raw data localized, thus promising to protect data privacy. Since its introduction, federated learning has undergone several evolutions. Most importantly, its evolution is in response to the growing recognition that its promise of collaborative learning is inseparable from the imperatives of privacy preservation and model security. The resource is divided into four parts. Part 1 (Security and Privacy) explores the robust defense mechanisms against targeted attacks and addresses fairness concerns, providing a multifaceted foundation for securing Federated Learning systems against evolving threats. Part 2 (Bilevel Optimization) unravels the intricacies of optimizing performance in federated settings. Part 3 (Graph and Large Language Models) addresses the challenges in training Graph Neural Networks and ensuring privacy in Federated Learning of natural language models. Part 4 (Edge Intelligence and Applications) demonstrates how Federated Learning can empower mobile applications and preserve privacy with synthetic data.
Federated Learning
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Author : Heiko Ludwig
language : en
Publisher: Springer Nature
Release Date : 2022-07-07
Federated Learning written by Heiko Ludwig and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-07 with Computers categories.
Federated Learning: A Comprehensive Overview of Methods and Applications presents an in-depth discussion of the most important issues and approaches to federated learning for researchers and practitioners. Federated Learning (FL) is an approach to machine learning in which the training data are not managed centrally. Data are retained by data parties that participate in the FL process and are not shared with any other entity. This makes FL an increasingly popular solution for machine learning tasks for which bringing data together in a centralized repository is problematic, either for privacy, regulatory or practical reasons. This book explains recent progress in research and the state-of-the-art development of Federated Learning (FL), from the initial conception of the field to first applications and commercial use. To obtain this broad and deep overview, leading researchers address the different perspectives of federated learning: the core machine learning perspective, privacy and security, distributed systems, and specific application domains. Readers learn about the challenges faced in each of these areas, how they are interconnected, and how they are solved by state-of-the-art methods. Following an overview on federated learning basics in the introduction, over the following 24 chapters, the reader will dive deeply into various topics. A first part addresses algorithmic questions of solving different machine learning tasks in a federated way, how to train efficiently, at scale, and fairly. Another part focuses on providing clarity on how to select privacy and security solutions in a way that can be tailored to specific use cases, while yet another considers the pragmatics of the systems where the federated learning process will run. The book also covers other important use cases for federated learning such as split learning and vertical federated learning. Finally, the book includes some chapters focusing on applying FL in real-world enterprise settings.
The Ai Book
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Author : Ivana Bartoletti
language : en
Publisher: John Wiley & Sons
Release Date : 2020-06-29
The Ai Book written by Ivana Bartoletti and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Business & Economics categories.
Written by prominent thought leaders in the global fintech space, The AI Book aggregates diverse expertise into a single, informative volume and explains what artifical intelligence really means and how it can be used across financial services today. Key industry developments are explained in detail, and critical insights from cutting-edge practitioners offer first-hand information and lessons learned. Coverage includes: · Understanding the AI Portfolio: from machine learning to chatbots, to natural language processing (NLP); a deep dive into the Machine Intelligence Landscape; essentials on core technologies, rethinking enterprise, rethinking industries, rethinking humans; quantum computing and next-generation AI · AI experimentation and embedded usage, and the change in business model, value proposition, organisation, customer and co-worker experiences in today’s Financial Services Industry · The future state of financial services and capital markets – what’s next for the real-world implementation of AITech? · The innovating customer – users are not waiting for the financial services industry to work out how AI can re-shape their sector, profitability and competitiveness · Boardroom issues created and magnified by AI trends, including conduct, regulation & oversight in an algo-driven world, cybersecurity, diversity & inclusion, data privacy, the ‘unbundled corporation’ & the future of work, social responsibility, sustainability, and the new leadership imperatives · Ethical considerations of deploying Al solutions and why explainable Al is so important
Federated Learning For Iot Applications
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Author : Satya Prakash Yadav
language : en
Publisher: Springer
Release Date : 2022-03-03
Federated Learning For Iot Applications written by Satya Prakash Yadav and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-03 with Technology & Engineering categories.
This book presents how federated learning helps to understand and learn from user activity in Internet of Things (IoT) applications while protecting user privacy. The authors first show how federated learning provides a unique way to build personalized models using data without intruding on users’ privacy. The authors then provide a comprehensive survey of state-of-the-art research on federated learning, giving the reader a general overview of the field. The book also investigates how a personalized federated learning framework is needed in cloud-edge architecture as well as in wireless-edge architecture for intelligent IoT applications. To cope with the heterogeneity issues in IoT environments, the book investigates emerging personalized federated learning methods that are able to mitigate the negative effects caused by heterogeneities in different aspects. The book provides case studies of IoT based human activity recognition to demonstrate the effectiveness of personalized federated learning for intelligent IoT applications, as well as multiple controller design and system analysis tools including model predictive control, linear matrix inequalities, optimal control, etc. This unique and complete co-design framework will benefit researchers, graduate students and engineers in the fields of control theory and engineering.
Federated Learning
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Author : Qiang Yang
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
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 2022-06-01 with Computers categories.
How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
Handbook Of Research On Deep Learning Techniques For Cloud Based Industrial Iot
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Author : Swarnalatha, P.
language : en
Publisher: IGI Global
Release Date : 2023-07-03
Handbook Of Research On Deep Learning Techniques For Cloud Based Industrial Iot written by Swarnalatha, P. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-03 with Computers categories.
Today’s business world is changing with the adoption of the internet of things (IoT). IoT is helping in prominently capturing a tremendous amount of data from multiple sources. Realizing the future and full potential of IoT devices will require an investment in new technologies. The Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT demonstrates how the computer scientists and engineers of today might employ artificial intelligence in practical applications with the emerging cloud and IoT technologies. The book also gathers recent research works in emerging artificial intelligence methods and applications for processing and storing the data generated from the cloud-based internet of things. Covering key topics such as data, cybersecurity, blockchain, and artificial intelligence, this premier reference source is ideal for industry professionals, engineers, computer scientists, researchers, scholars, academicians, practitioners, instructors, and students.
A Handbook On Modern Databases
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Author : Afifa Salsabil Fathima
language : en
Publisher: MileStone Research Publications
Release Date : 2025-02-03
A Handbook On Modern Databases written by Afifa Salsabil Fathima and has been published by MileStone Research Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Computers categories.
A comprehensive understanding of SQL is imperative for individuals involved in database-related tasks, be it application development, business intelligence, or data analysis. Proficiency in writing effective SQL queries not only enables users to extract meaningful insights from data but also ensures the efficient management of database resources. The knowledge gained from this chapter forms a solid foundation for adept database management, contributing to the overall success of data-driven applications and decision-making processes.
Handbook Of Research On Digital Transformation And Challenges To Data Security And Privacy
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Author : Anunciação, Pedro Fernandes
language : en
Publisher: IGI Global
Release Date : 2021-02-19
Handbook Of Research On Digital Transformation And Challenges To Data Security And Privacy written by Anunciação, Pedro Fernandes and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-19 with Computers categories.
Heavily dominated by the sector of information and communication technologies, economic organizations pursue digital transformation as a differentiating factor and source of competitive advantage. Understanding the challenges of digital transformation is critical to managers to ensure business sustainability. However, there are some problems, such as architecture, security, and reliability, among others, that bring with them the need for studies and investments in this area to avoid significant financial losses. Digital transformation encompasses and challenges many areas, such as business models, organizational structures, human privacy, management, and more, creating a need to investigate the challenges associated with it to create a roadmap for this new digital transformation era. The Handbook of Research on Digital Transformation and Challenges to Data Security and Privacy presents the main challenges of digital transformation and the threats it poses to information security and privacy, as well as models that can contribute to solving these challenges in economic organizations. While highlighting topics such as information systems, digital trends, and information governance, this book is ideally intended for managers, data analysts, cybersecurity professionals, IT specialists, practitioners, researchers, academicians, and students working in fields that include digital transformation, information management, information security, information system reliability, business continuity, and data protection.