LEADER 03954nam 22006135 450 001 9910627280003321 005 20251113181218.0 010 $a3-031-11748-4 024 7 $a10.1007/978-3-031-11748-0 035 $a(MiAaPQ)EBC7102608 035 $a(Au-PeEL)EBL7102608 035 $a(CKB)24959555200041 035 $a(PPN)264955137 035 $a(OCoLC)1348487546 035 $a(DE-He213)978-3-031-11748-0 035 $a(EXLCZ)9924959555200041 100 $a20220930d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFederated and Transfer Learning /$fedited by Roozbeh Razavi-Far, Boyu Wang, Matthew E. Taylor, Qiang Yang 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (371 pages) 225 1 $aAdaptation, Learning, and Optimization,$x1867-4542 ;$v27 311 08$aPrint version: Razavi-Far, Roozbeh Federated and Transfer Learning Cham : Springer International Publishing AG,c2022 9783031117473 320 $aIncludes bibliographical references. 327 $aAn Introduction to Federated and Transfer Learning -- Federated Learning for Resource-Constrained IoT Devices: Panoramas and State of the Art -- Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms -- Cross-silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems -- A Unifying Framework for Federated Learning -- A Contract Theory based Incentive Mechanism for Federated Learning -- A Study of Blockchain-based Federated Learning -- Swarm Meta Learning -- Rethinking Importance Weighting for Transfer Learning -- Transfer Learning via Representation Learning -- Modeling Individual Humans via a Secondary Task Transfer Learning Method -- From Theoretical to Practical Transfer Learning: The Adapt Library -- Lyapunov Robust Constrained-MDPs for Sim2Real Transfer Learning -- A Study on Efficient Reinforcement Learning Through Knowledge Transfer -- Federated Transfer Reinforcement Learning for Autonomous Driving. 330 $aThis book provides a collection of recent research works on learning from decentralized data, transferring information from one domain to another, and addressing theoretical issues on improving the privacy and incentive factors of federated learning as well as its connection with transfer learning and reinforcement learning. Over the last few years, the machine learning community has become fascinated by federated and transfer learning. Transfer and federated learning have achieved great success and popularity in many different fields of application. The intended audience of this book is students and academics aiming to apply federated and transfer learning to solve different kinds of real-world problems, as well as scientists, researchers, and practitioners in AI industries, autonomous vehicles, and cyber-physical systems who wish to pursue new scientific innovations and update their knowledge on federated and transfer learning and their applications. 410 0$aAdaptation, Learning, and Optimization,$x1867-4542 ;$v27 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aMachine learning 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aMachine Learning 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aMachine Learning. 676 $a780 676 $a006.31 702 $aRazavi-Far$b Roozbeh 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910627280003321 996 $aFederated and transfer learning$93020092 997 $aUNINA