LEADER 03538nam 22006375 450 001 9910633937203321 005 20251113173845.0 010 $a9789811970832 010 $a9811970831 024 7 $a10.1007/978-981-19-7083-2 035 $a(MiAaPQ)EBC7150304 035 $a(Au-PeEL)EBL7150304 035 $a(CKB)25504476700041 035 $a(PPN)266353274 035 $a(OCoLC)1352972196 035 $a(DE-He213)978-981-19-7083-2 035 $a(EXLCZ)9925504476700041 100 $a20221129d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFederated Learning $eFundamentals and Advances /$fby Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2023. 215 $a1 online resource (227 pages) 225 1 $aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 311 08$aPrint version: Jin, Yaochu Federated Learning Singapore : Springer,c2023 9789811970825 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Communication-Efficient Federated Learning -- Evolutionary Federated Learning.-Secure Federated Learning -- Summary and Outlook. 330 $aThis book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. . 410 0$aMachine Learning: Foundations, Methodologies, and Applications,$x2730-9916 606 $aMachine learning 606 $aData protection$xLaw and legislation 606 $aCryptography 606 $aData encryption (Computer science) 606 $aMachine Learning 606 $aPrivacy 606 $aCryptology 615 0$aMachine learning. 615 0$aData protection$xLaw and legislation. 615 0$aCryptography. 615 0$aData encryption (Computer science) 615 14$aMachine Learning. 615 24$aPrivacy. 615 24$aCryptology. 676 $a006.31 700 $aJin$b Yaochu$f1966-$0977855 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910633937203321 996 $aFederated learning$93089270 997 $aUNINA