Central Library

Federated Learning (Record no. 89442)

000 -LEADER
fixed length control field 05194nam a22006255i 4500
001 - CONTROL NUMBER
control field 978-3-030-63076-8
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730174449.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201125s2020 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030630768
-- 978-3-030-63076-8
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-030-63076-8
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA347.A78
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
245 10 - TITLE STATEMENT
Title Federated Learning
Medium [electronic resource] :
Remainder of title Privacy and Incentive /
Statement of responsibility, etc. edited by Qiang Yang, Lixin Fan, Han Yu.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2020.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Cham :
Name of producer, publisher, distributor, manufacturer Springer International Publishing :
-- Imprint: Springer,
Date of production, publication, distribution, manufacture, or copyright notice 2020.
300 ## - PHYSICAL DESCRIPTION
Extent X, 286 p. 94 illus., 82 illus. in color.
Other physical details online resource.
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
347 ## - DIGITAL FILE CHARACTERISTICS
File type text file
Encoding format PDF
Source rda
490 1# - SERIES STATEMENT
Series statement Lecture Notes in Artificial Intelligence,
International Standard Serial Number 2945-9141 ;
Volume/sequential designation 12500
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Privacy -- Threats to Federated Learning -- Rethinking Gradients Safety in Federated Learning -- Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks -- Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning -- Large-Scale Kernel Method for Vertical Federated Learning -- Towards Byzantine-resilient Federated Learning via Group-wise Robust Aggregation -- Federated Soft Gradient Boosting Machine for Streaming Data -- Dealing with Label Quality Disparity In Federated Learning -- Incentive -- FedCoin: A Peer-to-Peer Payment System for Federated Learning -- Efficient and Fair Data Valuation for Horizontal Federated Learning -- A Principled Approach to Data Valuation for Federated Learning -- A Gamified Research Tool for Incentive Mechanism Design in Federated Learning -- Budget-bounded Incentives for Federated Learning -- Collaborative Fairness in Federated Learning -- A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning -- Applications -- Federated Recommendation Systems -- Federated Learning for Open Banking -- Building ICU In-hospital Mortality Prediction Model with Federated Learning -- Privacy-preserving Stacking with Application to Cross-organizational Diabetes Prediction. .
520 ## - SUMMARY, ETC.
Summary, etc. This book provides a comprehensive and self-contained introduction to Federated Learning, ranging from the basic knowledge and theories to various key applications, and the privacy and incentive factors are the focus of the whole book. This book is timely needed since Federated Learning is getting popular after the release of the General Data Protection Regulation (GDPR). As Federated Learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. First, it introduces different privacy-preserving methods for protecting a Federated Learning model against different types of attacks such as Data Leakage and/or Data Poisoning. Second, the book presents incentive mechanisms which aim to encourage individuals to participate in the Federated Learning ecosystems. Last but not the least, this book also describeshow Federated Learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both academia and industries, who would like to learn federated learning from scratch, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing are preferred.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial intelligence.
9 (RLIN) 3407
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data protection.
9 (RLIN) 7245
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer networks .
9 (RLIN) 31572
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Social sciences
General subdivision Data processing.
9 (RLIN) 83360
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Application software.
9 (RLIN) 112599
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial Intelligence.
9 (RLIN) 3407
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data and Information Security.
9 (RLIN) 31990
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer Communication Networks.
9 (RLIN) 112600
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer Application in Social and Behavioral Sciences.
9 (RLIN) 31815
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer and Information Systems Applications.
9 (RLIN) 112601
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Yang, Qiang.
Relator term editor.
Relationship edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 112602
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Fan, Lixin.
Relator term editor.
Relationship edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 112603
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Yu, Han.
Relator term editor.
Relationship edt
-- http://id.loc.gov/vocabulary/relators/edt
9 (RLIN) 112604
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
9 (RLIN) 112605
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783030630751
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Printed edition:
International Standard Book Number 9783030630775
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Lecture Notes in Artificial Intelligence,
International Standard Serial Number 2945-9141 ;
Volume/sequential designation 12500
9 (RLIN) 112606
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-030-63076-8">https://doi.org/10.1007/978-3-030-63076-8</a>
912 ## -
-- ZDB-2-SCS
912 ## -
-- ZDB-2-SXCS
912 ## -
-- ZDB-2-LNC
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-Lecture Notes in CS

No items available.