Jin Y Federated Learning Fundamentals and Advances 2022
Category: eBooks
- views: 433
- date: 1 December 2022
- posted by: BeMyLove
Jin Y Federated Learning Fundamentals and Advances 2022 | 5.85 MB
English | 227 Pages
Title: Federated Learning: Fundamentals and Advances (Machine Learning: Foundations, Methodologies, and Applications)
Author: Yaochu Jin
Year: 2022
Description:
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.
DOWNLOAD:
https://rapidgator.net/file/9da972ab046bcc53959cfd57abb3c5ab/Jin_Y._Federated_Learning._Fundamentals_and_Advances_2022.rar
https://uploadgig.com/file/download/99D777719782a2dc/Jin_Y._Federated_Learning._Fundamentals_and_Advances_2022.rar
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.
DOWNLOAD:
https://rapidgator.net/file/9da972ab046bcc53959cfd57abb3c5ab/Jin_Y._Federated_Learning._Fundamentals_and_Advances_2022.rar
https://uploadgig.com/file/download/99D777719782a2dc/Jin_Y._Federated_Learning._Fundamentals_and_Advances_2022.rar
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