000 | 03735nam a22005895i 4500 | ||
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001 | 978-981-19-7083-2 | ||
003 | DE-He213 | ||
005 | 20240423125511.0 | ||
007 | cr nn 008mamaa | ||
008 | 221129s2023 si | s |||| 0|eng d | ||
020 |
_a9789811970832 _9978-981-19-7083-2 |
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024 | 7 |
_a10.1007/978-981-19-7083-2 _2doi |
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_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. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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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 _d178616 |