東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Cross-device federated recommendatio...

Kong, Xiangjie.

Linked to FindBook

Google Book

Amazon

博客來

Cross-device federated recommendation = privacy-preserving personalization /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cross-device federated recommendation/ by Xiangjie Kong ... [et al.].
Reminder of title:	privacy-preserving personalization /
other author:	Kong, Xiangjie.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xiii, 157 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Learning Paradigms in Cross-device Federated Recommendation -- Chapter 3. Privacy Computing in Cross-device Federated Recommendation -- Chapter 4. Federated Issues in Cross-device Federated Recommendation -- Chapter 5. Future Prospects.
Contained By:	Springer Nature eBook
Subject:	Federated learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-981-96-3212-1
ISBN:	9789819632121

Cross-device federated recommendation = privacy-preserving personalization /
Cross-device federated recommendationprivacy-preserving personalization /[electronic resource] :by Xiangjie Kong ... [et al.]. - Singapore :Springer Nature Singapore :2025. - xiii, 157 p. :ill. (chiefly color), digital ;24 cm. - Machine learning: foundations, methodologies, and applications,2730-9916. - Machine learning: foundations, methodologies, and applications..

Chapter 1. Introduction -- Chapter 2. Learning Paradigms in Cross-device Federated Recommendation -- Chapter 3. Privacy Computing in Cross-device Federated Recommendation -- Chapter 4. Federated Issues in Cross-device Federated Recommendation -- Chapter 5. Future Prospects.

This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed computing paradigm, cross-device federated learning enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant. This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point. This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence.

ISBN: 9789819632121

Standard No.: 10.1007/978-981-96-3212-1doiSubjects--Topical Terms:

3781177
Federated learning (Machine learning)

LC Class. No.: Q325.65

Dewey Class. No.: 006.31

Cross-device federated recommendation = privacy-preserving personalization /
LDR:03374nmm a2200361 a 4500 001 2408586
003 DE-He213
005 20250306115245.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819632121 $q (electronic bk.)
020 $a 9789819632114 $q (paper)
024 7 $a 10.1007/978-981-96-3212-1 $2 doi
035 $a 978-981-96-3212-1
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.65
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.65 $b .C951 2025
245 0 0 $a Cross-device federated recommendation $h [electronic resource] : $b privacy-preserving personalization / $c by Xiangjie Kong ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xiii, 157 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Machine learning: foundations, methodologies, and applications, $x 2730-9916
505 0 $a Chapter 1. Introduction -- Chapter 2. Learning Paradigms in Cross-device Federated Recommendation -- Chapter 3. Privacy Computing in Cross-device Federated Recommendation -- Chapter 4. Federated Issues in Cross-device Federated Recommendation -- Chapter 5. Future Prospects.
520 $a This book introduces the prevailing domains of recommender systems and cross-device federated learning, highlighting the latest research progress and prospects regarding cross-device federated recommendation. As a privacy-oriented distributed computing paradigm, cross-device federated learning enables collaborative intelligence across multiple devices while ensuring the security of local data. In this context, ubiquitous recommendation services emerge as a crucial application of device-side AI, making a deep exploration of federated recommendation systems highly significant. This book is self-contained, and each chapter can be comprehended independently. Overall, the book organizes existing efforts in federated recommendation from three different perspectives. The perspective of learning paradigms includes statistical machine learning, deep learning, reinforcement learning, and meta learning, where each has detailed techniques (e.g., different neural building blocks) to present relevant studies. The perspective of privacy computing covers homomorphic encryption, differential privacy, secure multi-party computing, and malicious attacks. More specific encryption and obfuscation techniques, such as randomized response and secret sharing, are involved. The perspective of federated issues discusses communication optimization and fairness perception, which are widely concerned in the cross-device distributed environment. In the end, potential issues and promising directions for future research are identified point by point. This book is especially suitable for researchers working on the application of recommendation algorithms to the privacy-preserving federated scenario. The target audience includes graduate students, academic researchers, and industrial practitioners who specialize in recommender systems, distributed machine learning, information retrieval, information security, or artificial intelligence.
650 0 $a Federated learning (Machine learning) $3 3781177
650 0 $a Recommender systems (Information filtering) $3 1002434
650 0 $a Data privacy. $3 3593153
650 1 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Privacy. $3 528582
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Intelligence Infrastructure. $3 3593599
650 2 4 $a Knowledge Based Systems. $3 3538738
700 1 $a Kong, Xiangjie. $3 3781176
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Machine learning: foundations, methodologies, and applications. $3 3531412
856 4 0 $u https://doi.org/10.1007/978-981-96-3212-1
950 $a Computer Science (SpringerNature-11645)