LEADER 04351oam 2200541 450 001 996418217803316 005 20210524001225.0 010 $a3-030-63076-5 024 7 $a10.1007/978-3-030-63076-8 035 $a(CKB)4100000011610157 035 $a(MiAaPQ)EBC6414084 035 $a(DE-He213)978-3-030-63076-8 035 $a(PPN)252506855 035 $a(EXLCZ)994100000011610157 100 $a20210524d2020 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aFederated learning $eprivacy and incentive /$fedited by Qiang Yang, Lixin Fan, and Han Yu 205 $a1st ed. 2020. 210 1$aCham, Switzerland :$cSpringer,$d[2020] 210 4$d©2020 215 $a1 online resource (X, 286 p. 94 illus., 82 illus. in color.) 225 1 $aLecture Notes in Artificial Intelligence ;$v12500 300 $aIncludes index. 311 $a3-030-63075-7 327 $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. . 330 $aThis book provides a comprehensive and self-contained introduction to Federated Learning, ranging from the basic knowledge and theories to various key applications, and the privacy and incentive factors are the focus of the whole book. This book is timely needed since Federated Learning is getting popular after the release of the General Data Protection Regulation (GDPR). As Federated Learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. First, it introduces different privacy-preserving methods for protecting a Federated Learning model against different types of attacks such as Data Leakage and/or Data Poisoning. Second, the book presents incentive mechanisms which aim to encourage individuals to participate in the Federated Learning ecosystems. Last but not the least, this book also describes how Federated Learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both academia and industries, who would like to learn federated learning from scratch, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing are preferred. 410 0$aLecture Notes in Artificial Intelligence ;$v12500 606 $aFederated database systems 606 $aApplication software 606 $aMachine learning 615 0$aFederated database systems. 615 0$aApplication software. 615 0$aMachine learning. 676 $a006.31 702 $aYu$b Han 702 $aFan$b Lixin 702 $aYang$b Qiang$f1961- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a996418217803316 996 $aFederated Learning$92899543 997 $aUNISA