東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Handbook of trustworthy federated learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Handbook of trustworthy federated learning/ edited by My T. Thai, Hai N. Phan, Bhavani Thuraisingham.
其他作者:	Thai, My T.
出版者:	Cham :Springer International Publishing : : 2025.,
面頁冊數:	x, 428 p. :ill. (chiefly color), digital ;24 cm.
內容註:	Trustworthiness, Privacy and Security in Federated Learning. - Secure Federated Learning -- Data Poisoning and Leakage Analysis in Federated Learning -- Robust Federated Learning against Targeted Attackers using Model Updates Correlation -- Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness -- Federated Bilevel Optimization -- A Two-Stage Stochastic Programming Approach for the Key Management 𝒒-Composite Scheme -- Recent Advances in Federated Graph Learning -- Privacy in Federated Learning Natural Language Models -- Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs -- Robust Federated Learning for Edge Intelligence -- ZoneFL: Zone-based Federated Learning at the Edge -- Synthetic Data for Privacy Preservation in Distributed Data Analysis Systems -- Towards Green Federated Learning.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-031-58923-2
ISBN:	9783031589232

Handbook of trustworthy federated learning
Handbook of trustworthy federated learning[electronic resource] /edited by My T. Thai, Hai N. Phan, Bhavani Thuraisingham. - Cham :Springer International Publishing :2025. - x, 428 p. :ill. (chiefly color), digital ;24 cm. - Springer optimization and its applications,v. 2131931-6836 ;. - Springer optimization and its applications ;v. 213..

Trustworthiness, Privacy and Security in Federated Learning. - Secure Federated Learning -- Data Poisoning and Leakage Analysis in Federated Learning -- Robust Federated Learning against Targeted Attackers using Model Updates Correlation -- Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness -- Federated Bilevel Optimization -- A Two-Stage Stochastic Programming Approach for the Key Management ��-Composite Scheme -- Recent Advances in Federated Graph Learning -- Privacy in Federated Learning Natural Language Models -- Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs -- Robust Federated Learning for Edge Intelligence -- ZoneFL: Zone-based Federated Learning at the Edge -- Synthetic Data for Privacy Preservation in Distributed Data Analysis Systems -- Towards Green Federated Learning.

This Handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on Federated Learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various topics. The readership spans from researchers and academics to practitioners who are deeply engaged or are starting to venture into the realms of Trustworthy Federated Learning. First introduced in 2016, federated learning allows devices to collaboratively learn a shared model while keeping raw data localized, thus promising to protect data privacy. Since its introduction, federated learning has undergone several evolutions. Most importantly, its evolution is in response to the growing recognition that its promise of collaborative learning is inseparable from the imperatives of privacy preservation and model security. The resource is divided into four parts. Part 1 (Security and Privacy) explores the robust defense mechanisms against targeted attacks and addresses fairness concerns, providing a multifaceted foundation for securing Federated Learning systems against evolving threats. Part 2 (Bilevel Optimization) unravels the intricacies of optimizing performance in federated settings. Part 3 (Graph and Large Language Models) addresses the challenges in training Graph Neural Networks and ensuring privacy in Federated Learning of natural language models. Part 4 (Edge Intelligence and Applications) demonstrates how Federated Learning can empower mobile applications and preserve privacy with synthetic data.

ISBN: 9783031589232

Standard No.: 10.1007/978-3-031-58923-2doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Handbook of trustworthy federated learning
LDR:03601nmm a2200361 a 4500 001 2407697
003 DE-He213
005 20240903130511.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031589232 $q (electronic bk.)
020 $a 9783031589225 $q (paper)
024 7 $a 10.1007/978-3-031-58923-2 $2 doi
035 $a 978-3-031-58923-2
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UR $2 bicssc
072 7 $a UTN $2 bicssc
072 7 $a COM053000 $2 bisacsh
072 7 $a UR $2 thema
072 7 $a UTN $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .H236 2025
245 0 0 $a Handbook of trustworthy federated learning $h [electronic resource] / $c edited by My T. Thai, Hai N. Phan, Bhavani Thuraisingham.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2025.
300 $a x, 428 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Springer optimization and its applications, $x 1931-6836 ; $v v. 213
505 0 $a Trustworthiness, Privacy and Security in Federated Learning. - Secure Federated Learning -- Data Poisoning and Leakage Analysis in Federated Learning -- Robust Federated Learning against Targeted Attackers using Model Updates Correlation -- Un-Fair Trojan: Targeted Backdoor Attacks Against Model Fairness -- Federated Bilevel Optimization -- A Two-Stage Stochastic Programming Approach for the Key Management 𝒒-Composite Scheme -- Recent Advances in Federated Graph Learning -- Privacy in Federated Learning Natural Language Models -- Federated Learning of Models Pre-Trained on Different Features with Consensus Graphs -- Robust Federated Learning for Edge Intelligence -- ZoneFL: Zone-based Federated Learning at the Edge -- Synthetic Data for Privacy Preservation in Distributed Data Analysis Systems -- Towards Green Federated Learning.
520 $a This Handbook aims to serve as a one-stop, reliable resource, including curated surveys and expository contributions on Federated Learning. It covers a comprehensive range of topics, providing the reader with technical and non-technical fundamentals, applications, and extensive details of various topics. The readership spans from researchers and academics to practitioners who are deeply engaged or are starting to venture into the realms of Trustworthy Federated Learning. First introduced in 2016, federated learning allows devices to collaboratively learn a shared model while keeping raw data localized, thus promising to protect data privacy. Since its introduction, federated learning has undergone several evolutions. Most importantly, its evolution is in response to the growing recognition that its promise of collaborative learning is inseparable from the imperatives of privacy preservation and model security. The resource is divided into four parts. Part 1 (Security and Privacy) explores the robust defense mechanisms against targeted attacks and addresses fairness concerns, providing a multifaceted foundation for securing Federated Learning systems against evolving threats. Part 2 (Bilevel Optimization) unravels the intricacies of optimizing performance in federated settings. Part 3 (Graph and Large Language Models) addresses the challenges in training Graph Neural Networks and ensuring privacy in Federated Learning of natural language models. Part 4 (Edge Intelligence and Applications) demonstrates how Federated Learning can empower mobile applications and preserve privacy with synthetic data.
650 0 $a Machine learning. $3 533906
650 0 $a Federated database systems. $3 3512038
650 1 4 $a Data and Information Security. $3 3538510
650 2 4 $a Optimization. $3 891104
650 2 4 $a Machine Learning. $3 3382522
700 1 $a Thai, My T. $3 901089
700 1 $a Phan, Hai N. $3 3779669
700 1 $a Thuraisingham, Bhavani. $3 2165820
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer optimization and its applications ; $v v. 213. $3 3779670
856 4 0 $u https://doi.org/10.1007/978-3-031-58923-2
950 $a Computer Science (SpringerNature-11645)