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001 978-981-19-7083-2
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020 _a9789811970832
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024 7 _a10.1007/978-981-19-7083-2
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072 7 _aMAT029000
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082 0 4 _a006.31
_223
100 1 _aJin, Yaochu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aFederated Learning
_h[electronic resource] :
_bFundamentals and Advances /
_cby Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXI, 218 p. 101 illus., 69 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
505 0 _aIntroduction -- Communication-Efficient Federated Learning -- Evolutionary Federated Learning.-Secure Federated Learning -- Summary and Outlook.
520 _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. .
650 0 _aMachine learning.
650 0 _aData protection
_xLaw and legislation.
650 0 _aCryptography.
650 0 _aData encryption (Computer science).
650 1 4 _aMachine Learning.
650 2 4 _aPrivacy.
650 2 4 _aCryptology.
700 1 _aZhu, Hangyu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aXu, Jinjin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aChen, Yang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811970825
776 0 8 _iPrinted edition:
_z9789811970849
776 0 8 _iPrinted edition:
_z9789811970856
830 0 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
856 4 0 _uhttps://doi.org/10.1007/978-981-19-7083-2
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178616
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