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008			220601s2020 sz | s |||| 0|eng d
020			_a9783031015854
024	7		_a10.1007/978-3-031-01585-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2020 EB
100	1		_aYang, Qiang _d1961- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687697
245	1	0	_aFederated Learning _cby Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu
250			_a1st edition 2020
264		1	_aCham _bSpringer International Publishing _c2020
300			_a1 recurso en línea (XVII, 189 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aPreface -- Acknowledgments -- Introduction -- Background -- Distributed Machine Learning -- Horizontal Federated Learning -- Vertical Federated Learning -- Federated Transfer Learning -- Incentive Mechanism Design for Federated Learning -- Federated Learning for Vision, Language, and Recommendation -- Federated Reinforcement Learning -- Selected Applications -- Summary and Outlook -- Bibliography -- Authors' Biographies.
520			_aHow is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.
988			_aSynthesis Collection of Technology_2020
650		7	_2embne _9138966 _aBases de datos
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9147793 _aProtección de datos
700	1		_aLiu, Yang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aCheng, Yong, _d1983- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687860
700	1		_aKang, Yan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687862
700	1		_aChen, Tianjian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687861
700	1		_aYu, Han _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687863
776	0	8	_iPrinted edition: _z9783031000300
776	0	8	_iPrinted edition: _z9783031004575
776	0	8	_iPrinted edition: _z9783031027130
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01585-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI