000			01792nam a2200301 i 4500
003			IN-BdCUP
005			20250103125020.0
008			200630s2022||||enk o ||1 0|eng|d
020			_a9781108955959 (ebook) _z9781108832373 (hardback)
040			_aIN-BdCUP _beng _cIN-BdCUP _erda
041			_aeng
050			_aQA76.583 _b.S66 2022
082			_a5.758
100			_aGuo, Song _eAuthor
245		0	_aEdge learning for distributed big data analytics : _btheory, algorithms, and system design / _cSong Guo, Zhihao Qu.
264			_aCambridge : _bCambridge University Press, _c2022
300			_a1 online resource (x, 217 pages) : _bdigital, PDF file(s).
336			_atext _btxt
337			_2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 21 Jan 2022).
520			_aDiscover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.
650			_aEdge computing.
700			_aQu, Zhihao, _eauthor.
776			_iPrint version: _z9781108955959
856			_3Electronic Book Resource _uhttps://doi.org/10.1017/9781108955959
942			_2ddc _cE
999			_c54648 _d54648