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020			_a9783030706043
024	7		_a10.1007/978-3-030-70604-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
245	1	0	_aFederated Learning Systems : _bTowards Next-Generation AI _cedited by Muhammad Habib ur Rehman, Mohamed Medhat Gaber.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XVI, 196 páginas) _b45 ilustraciones, 42 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aStudies in Computational Intelligence _x1860-9503 _v965
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aFederated Learning Research: Trends and Bibliometric Analysis -- A Review of Privacy-preserving Federated Learning for the Internet-of-Things -- Differentially Private Federated Learning: Algorithm, Analysis and Optimization -- Advancements of federated learning towards privacy preservation: from federated learning to split learning -- PySyft: A Library for Easy Federated Learning -- Federated Learning Systems for Healthcare: Perspective and Recent Progress -- Towards Blockchain-Based Fair and Trustworthy Federated Learning Systems -- An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies.
520	3		_aThis book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors' control of their critical data.
988			_aSpringer_Robotics_2021
650		7	_2embne _9166090 _aAprendizaje automático
700			_aRehman, Muhammad Habib ur _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682608
700			_aGaber, Mohamed Medhat _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9682609
776	0	8	_iPrinted edition: _z9783030706036
776	0	8	_iPrinted edition: _z9783030706050
776	0	8	_iPrinted edition: _z9783030706067
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-70604-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE