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003 DE-He213
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008 201125s2020 sz | s |||| 0|eng d
020 _a9783030630768
_9978-3-030-63076-8
024 7 _a10.1007/978-3-030-63076-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aFederated Learning
_h[electronic resource] :
_bPrivacy and Incentive /
_cedited by Qiang Yang, Lixin Fan, Han Yu.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aX, 286 p. 94 illus., 82 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v12500
505 0 _aPrivacy -- 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 _aThis 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 _aArtificial intelligence.
650 0 _aData protection.
650 0 _aComputer networks .
650 0 _aSocial sciences
_xData processing.
650 0 _aApplication software.
650 1 4 _aArtificial Intelligence.
650 2 4 _aData and Information Security.
650 2 4 _aComputer Communication Networks.
650 2 4 _aComputer Application in Social and Behavioral Sciences.
650 2 4 _aComputer and Information Systems Applications.
700 1 _aYang, Qiang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aFan, Lixin.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aYu, Han.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030630751
776 0 8 _iPrinted edition:
_z9783030630775
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v12500
856 4 0 _uhttps://doi.org/10.1007/978-3-030-63076-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
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