Автор: Swan, Jerry Nivel, Eric Kant, Neel Hedges, Jules Atkinson, Timothy Steunebrink, Bas Название: Formal Techniques for Distributed Objects, Components, and Systems ISBN: 3031086783 ISBN-13(EAN): 9783031086786 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The two-volume set LNCS 13341 and 13342 constitutes the refereed proceedings of the Joint International Conference on Digital Inclusion, Assistive Technology, and Accessibility, ICCHP-AAATE 2022. language accessibility for the deaf and hard-of-hearing.Part II: Digital accessibility: readability and understandability;
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: 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.
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.
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.
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: Verma, Dinesh C. Название: Federated ai for real-world business scenarios ISBN: 1032049359 ISBN-13(EAN): 9781032049359 Издательство: Taylor&Francis Рейтинг: Цена: 7961.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
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: 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: Lim Название: Federated Learning Over Wireless Edge Networks ISBN: 3031078373 ISBN-13(EAN): 9783031078378 Издательство: Springer Рейтинг: Цена: 13974.00 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: 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.
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