LEADER 00656nam0-22002411i-450- 001 990001137620403321 035 $a000113762 035 $aFED01000113762 035 $a(Aleph)000113762FED01 035 $a000113762 100 $a20000920d1970----km-y0itay50------ba 101 0 $aeng 200 1 $aProblems in Analysis$fby Bochner 210 $aNew Jersey$cPrinceton University Press$d1970 700 1$aBochner,$bSalomon$f<1899-1982>$040811 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001137620403321 952 $a203-C-25$b10665$fMA1 959 $aMA1 996 $aProblems in Analysis$9344388 997 $aUNINA DB $aING01 LEADER 03932nam 22006855 450 001 996517751703316 005 20230328095244.0 010 $a9783031289965$b(electronic bk.) 010 $z9783031289958 024 7 $a10.1007/978-3-031-28996-5 035 $a(MiAaPQ)EBC7221150 035 $a(Au-PeEL)EBL7221150 035 $a(OCoLC)1374425264 035 $a(DE-He213)978-3-031-28996-5 035 $a(PPN)26909282X 035 $a(EXLCZ)9926347442000041 100 $a20230328d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTrustworthy Federated Learning$b[electronic resource] $eFirst International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, Vienna, Austria, July 23, 2022, Revised Selected Papers /$fedited by Randy Goebel, Han Yu, Boi Faltings, Lixin Fan, Zehui Xiong 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (168 pages) $cillustrations 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13448 311 08$aPrint version: Goebel, Randy Trustworthy Federated Learning Cham : Springer International Publishing AG,c2023 9783031289958 320 $aIncludes bibliographical references and index. 327 $aAdaptive Expert Models for Personalization in Federated Learning -- Federated Learning with GAN-based Data Synthesis for Non-iid Clients -- Practical and Secure Federated Recommendation with Personalized Mask -- A General Theory for Client Sampling in Federated Learning -- Decentralized adaptive clustering of deep nets is beneficial for client collaboration -- Sketch to Skip and Select: Communication Efficient Federated Learning using Locality Sensitive Hashing -- Fast Server Learning Rate Tuning for Coded Federated Dropout -- FedAUXfdp: Differentially Private One-Shot Federated Distillation -- Secure forward aggregation for vertical federated neural network -- Two-phased Federated Learning with Clustering and Personalization for Natural Gas Load Forecasting -- Privacy-Preserving Federated Cross-Domain Social Recommendation. 330 $aThis book constitutes the refereed proceedings of the First International Workshop, FL 2022, Held in Conjunction with IJCAI 2022, held in Vienna, Austria, during July 23-25, 2022. The 11 full papers presented in this book were carefully reviewed and selected from 12 submissions. They are organized in three topical sections: answer set programming; adaptive expert models for personalization in federated learning and privacy-preserving federated cross-domain social recommendation. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13448 606 $aArtificial intelligence 606 $aData protection 606 $aSocial sciences?Data processing 606 $aApplication software 606 $aArtificial Intelligence 606 $aData and Information Security 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputer and Information Systems Applications 615 0$aArtificial intelligence. 615 0$aData protection. 615 0$aSocial sciences?Data processing. 615 0$aApplication software. 615 14$aArtificial Intelligence. 615 24$aData and Information Security. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputer and Information Systems Applications. 676 $a006.3 702 $aYu$b Han$c(Assistant Professor), 702 $aGoebel$b Randy 702 $aFaltings$b Boi 702 $aFan$b Lixin$c(Scientist), 702 $aXiong$b Zehui 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996517751703316 996 $aTrustworthy Federated Learning$93091291 997 $aUNISA