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: Rehman Muhammad Habib Ur, Gaber Mohamed Medhat Название: Federated Learning Systems: Towards Next-Generation AI ISBN: 3030706036 ISBN-13(EAN): 9783030706036 Издательство: Springer 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.
Описание: This book presents the proceedings of the International Conference SDOT which was organized at the University in ?ilina, Faculty of Management Sciences and Informatics, Slovak Republic in November 19, 2015. The conference was truly international both in terms of the amount of foreign contributions and in terms of composition of steering and scientific committees. The book and the conference serves as a platform of professional exchange of knowledge and experience for the latest trends in software development and object-oriented technologies (theory and practice). This proceedings present information on the latest developments and mediate the exchange of experience between practitioners and academia.
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: Есть у поставщика 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.
Modeling and analyzing resource-sensitive actors: A tutorial introduction.- Manifestly Phased Communication via Shared Session Types.- Deconfined Global Types for Asynchronous Sessions.- Relating Functional and Imperative Session Types.- Safe Session-Based Asynchronous Coordination in Rust.- A Session Subtyping Tool.- Towards Probabilistic Session-Type Monitoring.- Java Typestate Checker.- Asynchronous Global Types in co-logic Programming.- Tuple-Based Coordination in Large-Scale Situated Systems.- A Theory of Automated Market Makers in DeFi.- ReGraDa: Reactive Graph Data.- The Structure of Concurrent Process Histories.- A Clean and Efficient Implementation of Choreography Synthesis for Behavioural Contracts.- A Practical Tool-Chain for the Development of Coordination Scenarios: Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator.- Microservice Dynamic Architecture-Level Deployment Orchestration.- Jolie & LEMMA: Model-Driven Engineering and Programming Languages Meet on Microservices.- ScaFi-Web: a Web-Based Application for Field-Based Coordination Programming.
Автор: Yang Qiang, Fan Lixin, Yu Han Название: Federated Learning: Privacy and Incentive ISBN: 3030630757 ISBN-13(EAN): 9783030630751 Издательство: Springer Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications.
Описание: This book constitutes the refereed post-conference proceedings of the Second BenchCouncil International Federated Intelligent Computing and Block Chain Conferences, FICC 2020, held in Qingdao, China, in October/ November 2020.The 32 full papers and 6 short papers presented were carefully reviewed and selected from 103 submissions.
Автор: Ludwig Название: Federated Learning ISBN: 3030968952 ISBN-13(EAN): 9783030968953 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.
Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu Название: Federated Learning ISBN: 1681736977 ISBN-13(EAN): 9781681736976 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12335.00 р. Наличие на складе: Нет в наличии.
Описание: 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.
Автор: Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu Название: Federated Learning ISBN: 1681736993 ISBN-13(EAN): 9781681736990 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 15523.00 р. Наличие на складе: Нет в наличии.
Описание: 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.
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