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 LEADER 03629nam 2200949z- 450 001 9910557647803321 005 20220111 035 $a(CKB)5400000000044975 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/76645 035 $a(oapen)doab76645 035 $a(EXLCZ)995400000000044975 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRadiation Sensing: Design and Deployment of Sensors and Detectors 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 online resource (157 p.) 311 08$a3-0365-1440-6 311 08$a3-0365-1439-2 330 $aRadiation detection is important in many fields, and it poses significant challenges for instrument designers. Radiation detection instruments, particularly for nuclear decommissioning and security applications, are required to operate in unknown environments and should detect and characterise radiation fields in real time. This book covers both theory and practice, and it solicits recent advances in radiation detection, with a particular focus on radiation detection instrument design, real-time data processing, radiation simulation and experimental work, robot design, control systems, task planning and radiation shielding. 517 $aRadiation Sensing 606 $aTechnology: general issues$2bicssc 610 $aair insulation 610 $aBayesian inference 610 $aCompton edge reconstruction 610 $aCOTS commercial MAPS 610 $adeep autoencoder 610 $adeep learning 610 $adose rate uncertainty 610 $aenergy broadening correction 610 $aG(E) function 610 $again 610 $agamma ray detector 610 $agamma spectral analysis 610 $agamma-ray 610 $agaussian process regression 610 $aground-penetrating radar 610 $ahigh-energy ?-particle detection 610 $aillicit trafficking 610 $aintegral time 610 $alow voltage 610 $alow-level radioactive contaminants 610 $alow-resolution detector 610 $anational security 610 $aneutron 610 $anon-proliferation 610 $anuclear decommissioning 610 $anuclear wastes 610 $aoperational quantities 610 $apartial discharges 610 $apassive radiation detection 610 $aphotomultiplier 610 $aplastic gamma spectra 610 $aradiation detection 610 $aradiation response 610 $aradiation sensing technologies 610 $aradioactive nuclear waste 610 $aradioisotope identification 610 $aradiological characterisation 610 $aradiological characterization 610 $arapid prototyping 610 $areal-time dosimetry 610 $aremote depth profiling 610 $aremote-depth profiling 610 $arheology 610 $ascintillations 610 $asensor fusion 610 $aspectrum-to-dose conversion operator 610 $athick depletion width detectors 610 $auncertainty estimation 615 7$aTechnology: general issues 700 $aGamage$b Kelum$4edt$01325184 702 $aTaylor$b C. James$4edt 702 $aGamage$b Kelum$4oth 702 $aTaylor$b C. James$4oth 906 $aBOOK 912 $a9910557647803321 996 $aRadiation Sensing: Design and Deployment of Sensors and Detectors$93036656 997 $aUNINA