Federated ai for real-world business scenarios, Verma, Dinesh C.
Автор: Ahmed Bouajjani; Alexandra Silva Название: Formal Techniques for Distributed Objects, Components, and Systems ISBN: 3319602241 ISBN-13(EAN): 9783319602240 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Session types for Link failures.- Learning-based compositional parameter synthesis for event-recording automata.- Modularising opacity verification for Hybrid Transactional Memory.- Proving opacity via linearizability: a sound and complete method.- On futures for streaming data in ABS.- Session-based concurrency, reactively.- Procedural choreographic programming.- An observational approach to defining linearizability on weak memory models.- Applying a dependency mechanism in the formal development of voting protocol models using event-B.- Weak simulation quasimetric in a gossip scenario.- Reasoning about distributed secrets.- Classical higher-order processes.- Weak nominal modal logic.- Type inference of simulink hierarchical block diagrams in Isabelle.- Creating Bьchi automata for multi-valued model checking.- Privacy assessment using static taint analysis.- EPTL - a temporal logic for weakly consistent systems.
Автор: Verma Dinesh C. Название: Federated AI for Real-World Business Scenarios ISBN: 0367861577 ISBN-13(EAN): 9780367861575 Издательство: Taylor&Francis Рейтинг: Цена: 22968.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a holistic overview of all aspects of federated AI, which allows creation of real-world applications in contexts where data is dispersed in many different locations.
Автор: Al-Turjman Название: Real-Time Intelligence for Heterogeneous Networks ISBN: 3030756165 ISBN-13(EAN): 9783030756161 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses several exciting research topics and applications in the intelligent Heterogenous Networks (Het-Net) and Internet of Things (IoT) era. We are resolving significant issues towards realizing the future vision of the Artificial Intelligence (AI) in IoT-enabled spaces. Such AI-powered IoT solutions will be employed in satisfying critical conditions towards further advances in our daily smart life. This book overviews the associated issues and proposes the most up to date alternatives. The objective is to pave the way for AI-powered IoT-enabled spaces in the next generation Het-Net technologies and open the door for further innovations. The book presents the latest advances and research into heterogeneous networks in critical IoT applications. It discusses the most important problems, challenges, and issues that arise when designing real-time intelligent heterogeneous networks for diverse scenarios.
Автор: Arora, Ritu Название: Combinatorial Optimization Under Uncertainty ISBN: 1032316586 ISBN-13(EAN): 9781032316581 Издательство: Taylor&Francis Рейтинг: Цена: 12554.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book discusses several exciting research topics and applications in the intelligent Heterogenous Networks (Het-Net) and Internet of Things (IoT) era. The book presents the latest advances and research into heterogeneous networks in critical IoT applications.
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: Razavi-Far Название: Federated and Transfer Learning ISBN: 3031117476 ISBN-13(EAN): 9783031117473 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications.
Автор: Rehman Название: Federated Learning Systems ISBN: 3030706060 ISBN-13(EAN): 9783030706067 Издательство: Springer Рейтинг: Цена: 22359.00 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: This book constitutes the proceedings of the 22nd International Conference on Coordination Models and Languages, COORDINATION 2020, which was due to be held in Valletta, Malta, in June 2020, as part of the 15th International Federated Conference on Distributed Computing Techniques, DisCoTec 2020.
Название: Interactive theorem proving ISBN: 3319948202 ISBN-13(EAN): 9783319948201 Издательство: Springer Рейтинг: Цена: 12577.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 9th International Conference on Interactive Theorem Proving, ITP 2018, held in Oxford, UK, in July 2018. The 32 full papers and 5 short papers presented were carefully reviewed and selected from 65 submissions.
Автор: Weber, Felix Название: Artificial Intelligence for Business Analytics ISBN: 3658375981 ISBN-13(EAN): 9783658375980 Издательство: Springer Рейтинг: Цена: 9083.00 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: 20962.00 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.
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