Автор: Nguyen,Lam M. Название: Federated Learning ISBN: 0443190372 ISBN-13(EAN): 9780443190377 Издательство: Elsevier Science Рейтинг: Цена: 16161.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Yang, Qiang Liu, Yang Cheng, Yong Kang, Yan Chen, Tianjian Yu, Han Название: Federated Learning ISBN: 3031004574 ISBN-13(EAN): 9783031004575 Издательство: Springer Рейтинг: Цена: 7927.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Ludwig Название: Federated Learning ISBN: 3030968987 ISBN-13(EAN): 9783030968984 Издательство: Springer Рейтинг: Цена: 18294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Yu Название: Security and Privacy in Federated Learning ISBN: 9811986916 ISBN-13(EAN): 9789811986918 Издательство: Springer Рейтинг: Цена: 19514.00 р. Наличие на складе: Нет в наличии.
Описание: In this book, the authors highlight the latest research findings on the security and privacy of federated learning systems. The main attacks and counterattacks in this booming field are presented to readers in connection with inference, poisoning, generative adversarial networks, differential privacy, secure multi-party computation, homomorphic encryption, and shuffle, respectively. The book offers an essential overview for researchers who are new to the field, while also equipping them to explore this “uncharted territory.” For each topic, the authors first present the key concepts, followed by the most important issues and solutions, with appropriate references for further reading. The book is self-contained, and all chapters can be read independently. It offers a valuable resource for master’s students, upper undergraduates, Ph.D. students, and practicing engineers alike.
Автор: Jin Название: Federated Learning ISBN: 9811970823 ISBN-13(EAN): 9789811970825 Издательство: Springer Рейтинг: Цена: 19514.00 р. Наличие на складе: Нет в наличии.
Описание: This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionary learning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.
Автор: Lim Название: Federated Learning Over Wireless Edge Networks ISBN: 3031078373 ISBN-13(EAN): 9783031078378 Издательство: Springer Рейтинг: Цена: 12196.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Ludwig Название: Federated Learning ISBN: 3030968952 ISBN-13(EAN): 9783030968953 Издательство: Springer Рейтинг: Цена: 18294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Rehman Название: Federated Learning Systems ISBN: 3030706060 ISBN-13(EAN): 9783030706067 Издательство: Springer Рейтинг: Цена: 19514.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development.
Описание: Rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. The book provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication.
Описание: Rediscovers, redefines, and reestablishes the most recent applications of federated learning using blockchain and IIoT to optimize data for next-generation networks. The book provides insights to readers in a way of inculcating the theme that shapes the next generation of secure communication.
Автор: Adria Gascon, Aleksandra Korolova, Ananda Theertha Suresh, Arjun Nitin Bhagoji, Aurelien Bellet, Ayfer Ozgur, Badih Ghazi, Ben Hutchinson, Brendan Ave Название: Advances and Open Problems in Federated Learning ISBN: 1680837885 ISBN-13(EAN): 9781680837889 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 13721.00 р. Наличие на складе: Поставка под заказ.
Описание: The term Federated Learning was coined as recently as 2016 to describe a machine learning setting where multiple entities collaborate in solving a machine learning problem, under the coordination of a central server or service provider. This book describes the latest state-of-the art.
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