國立高雄大學圖資館 |

登入

回首頁

Federated learningfundamentals and advances /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Federated learningby Yaochu Jin ... [et al.].
其他題名:	fundamentals and advances /
其他作者:	Jin, Yaochu.
出版者:	Singapore :Springer Nature Singapore :2023.
面頁冊數:	xi, 218 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-981-19-7083-2
ISBN:	9789811970832$q(electronic bk.)

Federated learningfundamentals and advances /
Federated learningfundamentals and advances /[electronic resource] :by Yaochu Jin ... [et al.]. - Singapore :Springer Nature Singapore :2023. - xi, 218 p. :ill., digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

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.

ISBN: 9789811970832$q(electronic bk.)

Standard No.: 10.1007/978-981-19-7083-2doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Federated learningfundamentals and advances /
LDR:02556nmm a2200337 a 4500 001 644636
003 DE-He213
005 20221129195223.0
006 m d
007 cr nn 008maaau
008 231208s2023 si s 0 eng d
020 $a 9789811970832$q(electronic bk.)
020 $a 9789811970825$q(paper)
024 7 $a 10.1007/978-981-19-7083-2 $2 doi
035 $a 978-981-19-7083-2
040 $a GP $c GP $e rda
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQM $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQM $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .F293 2023
245 0 0 $a Federated learning $h [electronic resource] : $b fundamentals and advances / $c by Yaochu Jin ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a xi, 218 p. : $b ill., digital ; $c 24 cm.
490 1 $a Machine learning: foundations, methodologies, and applications, $x 2730-9916
505 0 $a Introduction -- Communication-Efficient Federated Learning -- Evolutionary Federated Learning -- Secure Federated Learning -- Summary and Outlook.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 1 4 $a Machine Learning. $3 833608
650 2 4 $a Privacy. $3 268519
650 2 4 $a Cryptology. $3 825728
700 1 $a Jin, Yaochu. $3 260756
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Machine learning: foundations, methodologies, and applications. $3 911919
856 4 0 $u https://doi.org/10.1007/978-981-19-7083-2
950 $a Computer Science (SpringerNature-11645)