Federated Learning [electronic resource] : Fundamentals and Advances / by Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen.

Por: Jin, Yaochu [author.]Colaborador(es): Zhu, Hangyu [author.] | Xu, Jinjin [author.] | Chen, Yang [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries Machine Learning: Foundations, Methodologies, and ApplicationsEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2023Edición: 1st ed. 2023Descripción: XI, 218 p. 101 illus., 69 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789811970832Tema(s): Machine learning | Data protection -- Law and legislation | Cryptography | Data encryption (Computer science) | Machine Learning | Privacy | CryptologyFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 006.31 Clasificación LoC:Q325.5-.7Recursos en línea: Libro electrónicoTexto
Contenidos:
Introduction -- Communication-Efficient Federated Learning -- Evolutionary Federated Learning.-Secure Federated Learning -- Summary and Outlook.
En: Springer Nature eBookResumen: 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. .
Star ratings
    Valoración media: 0.0 (0 votos)
Existencias
Tipo de ítem Biblioteca actual Colección Signatura Copia número Estado Fecha de vencimiento Código de barras
Libro Electrónico Biblioteca Electrónica
Colección de Libros Electrónicos 1 No para préstamo

Acceso multiusuario

Introduction -- Communication-Efficient Federated Learning -- Evolutionary Federated Learning.-Secure Federated Learning -- Summary and Outlook.

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. .

UABC ; Perpetuidad

Con tecnología Koha