000			03311nam a22004215i 4500
001			394068
003			ES-MaUEC
005			20230102123050.0
007			cr nn 008mamaa
008			220928s2022 sz | s |||| 0|eng d
020			_a9783031078385
024	7		_a10.1007/978-3-031-07838-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aLim, Wei Yang Bryan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aFederated Learning Over Wireless Edge Networks _cby Wei Yang Bryan Lim, Jer Shyuan Ng, Zehui Xiong, Dusit Niyato, Chunyan Miao
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XV, 165 páginas) _b51 ilustraciones, 47 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aWireless Networks _x2366-1445
505	0		_aFederated Learning at Mobile Edge Networks: A Tutorial -- Multi-Dimensional Contract Matching Design for Federated Learning in UAV Networks -- Joint Auction-Coalition Formation Framework for UAV-assisted Communication-Efficient Federated Learning -- Evolutionary Edge Association and Auction in Hierarchical Federated Learning -- Conclusion and Future Works.
520			_aThis book first presents a tutorial on Federated Learning (FL) and its role in enabling Edge Intelligence over wireless edge networks. This provides readers with a concise introduction to the challenges and state-of-the-art approaches towards implementing FL over the wireless edge network. Then, in consideration of resource heterogeneity at the network edge, the authors provide multifaceted solutions at the intersection of network economics, game theory, and machine learning towards improving the efficiency of resource allocation for FL over the wireless edge networks. A clear understanding of such issues and the presented theoretical studies will serve to guide practitioners and researchers in implementing resource-efficient FL systems and solving the open issues in FL respectively. Provides a concise introduction to Federated Learning (FL) and how it enables Edge Intelligence; Highlights the challenges inherent to achieving scalable implementation of FL at the wireless edge; Presents how FL can address challenges resulting from the confluence of AI and wireless communications.
700	1		_aNg, Jer Shyuan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aXiong, Zehui _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aNiyato, Dusit _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aMiao, Chunyan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031078378
776	0	8	_iPrinted edition: _z9783031078392
776	0	8	_iPrinted edition: _z9783031078408
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-07838-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394068 _d394068