Verfügbarkeit: Federated Learning | Technische Universität München

Federated Learning: Fundamentals and Advances

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Bibliographische Detailangaben
Beteiligte Personen:	Jin, Yaochu (VerfasserIn), Zhu, Hangyu (VerfasserIn), Xu, Jinjin (VerfasserIn), Chen, Yang (VerfasserIn)
Format:	Elektronisch E-Book
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 2023 Singapore Springer
Ausgabe:	1st ed. 2023
Schriftenreihe:	Machine Learning: Foundations, Methodologies, and Applications
Schlagwörter:	Machine Learning Privacy Cryptology Machine learning Data protection—Law and legislation Cryptography Data encryption (Computer science)
Links:	https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2 https://doi.org/10.1007/978-981-19-7083-2
Umfang:	1 Online-Ressource (XI, 218 p. 101 illus., 69 illus. in color)
ISBN:	9789811970832
ISSN:	2730-9916
DOI:	10.1007/978-981-19-7083-2

Internformat

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Datensatz im Suchindex

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id	DE-604.BV048603939
illustrated	Not Illustrated
indexdate	2024-12-20T19:49:46Z
institution	BVB
isbn	9789811970832
issn	2730-9916
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-033979414
oclc_num	1355301177
open_access_boolean
owner	DE-522 DE-858 DE-860 DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-188 DE-523 DE-1049 DE-859 DE-473 DE-BY-UBG DE-29 DE-863 DE-BY-FWS DE-1050 DE-20 DE-862 DE-BY-FWS DE-92 DE-355 DE-BY-UBR DE-824 DE-384 DE-M347 DE-573 DE-703 DE-706 DE-739 DE-634
owner_facet	DE-522 DE-858 DE-860 DE-19 DE-BY-UBM DE-Aug4 DE-898 DE-BY-UBR DE-861 DE-188 DE-523 DE-1049 DE-859 DE-473 DE-BY-UBG DE-29 DE-863 DE-BY-FWS DE-1050 DE-20 DE-862 DE-BY-FWS DE-92 DE-355 DE-BY-UBR DE-824 DE-384 DE-M347 DE-573 DE-703 DE-706 DE-739 DE-634
physical	1 Online-Ressource (XI, 218 p. 101 illus., 69 illus. in color)
psigel	ZDB-2-SCS ZDB-2-SCS_2023
publishDate	2023
publishDateSearch	2023
publishDateSort	2023
publisher	Springer Nature Singapore Springer
record_format	marc
series2	Machine Learning: Foundations, Methodologies, and Applications
spelling	Jin, Yaochu Verfasser aut Federated Learning Fundamentals and Advances by Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen 1st ed. 2023 Singapore Springer Nature Singapore 2023 Singapore Springer 1 Online-Ressource (XI, 218 p. 101 illus., 69 illus. in color) txt rdacontent c rdamedia cr rdacarrier Machine Learning: Foundations, Methodologies, and Applications 2730-9916 Machine Learning Privacy Cryptology Machine learning Data protection—Law and legislation Cryptography Data encryption (Computer science) Zhu, Hangyu aut Xu, Jinjin aut Chen, Yang aut Erscheint auch als Druck-Ausgabe 978-981-1970-82-5 Erscheint auch als Druck-Ausgabe 978-981-1970-84-9 Erscheint auch als Druck-Ausgabe 978-981-1970-85-6 https://doi.org/10.1007/978-981-19-7083-2 Verlag URL des Erstveröffentlichers Volltext
spellingShingle	Jin, Yaochu Zhu, Hangyu Xu, Jinjin Chen, Yang Federated Learning Fundamentals and Advances Machine Learning Privacy Cryptology Machine learning Data protection—Law and legislation Cryptography Data encryption (Computer science)
title	Federated Learning Fundamentals and Advances
title_auth	Federated Learning Fundamentals and Advances
title_exact_search	Federated Learning Fundamentals and Advances
title_full	Federated Learning Fundamentals and Advances by Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen
title_fullStr	Federated Learning Fundamentals and Advances by Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen
title_full_unstemmed	Federated Learning Fundamentals and Advances by Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen
title_short	Federated Learning
title_sort	federated learning fundamentals and advances
title_sub	Fundamentals and Advances
topic	Machine Learning Privacy Cryptology Machine learning Data protection—Law and legislation Cryptography Data encryption (Computer science)
topic_facet	Machine Learning Privacy Cryptology Machine learning Data protection—Law and legislation Cryptography Data encryption (Computer science)
url	https://doi.org/10.1007/978-981-19-7083-2
work_keys_str_mv	AT jinyaochu federatedlearningfundamentalsandadvances AT zhuhangyu federatedlearningfundamentalsandadvances AT xujinjin federatedlearningfundamentalsandadvances AT chenyang federatedlearningfundamentalsandadvances

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