000			02921nam a22003375i 4500
008			221129s2023 si | s |||| 0|eng d
020			_a9789811970832 _9978-981-19-7083-2
041			_aeng
082	0	4	_a006.31 _223
100	1		_aJin, Yaochu. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _91307489
245	1	0	_aFederated Learning _h[electronic resource] : _bFundamentals and Advances / _cby Yaochu Jin, Hangyu Zhu, Jinjin Xu, Yang Chen.
250			_a1st ed. 2023.
260		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2023.
300			_aXI, 218 p. 101 illus., 69 illus. in color. _bonline resource.
490	1		_aMachine Learning: Foundations, Methodologies, and Applications, _x2730-9916
505	0		_aIntroduction -- Communication-Efficient Federated Learning -- Evolutionary Federated Learning.-Secure Federated Learning -- Summary and Outlook.
520			_aThis book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation. The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses. .
650		0	_aMachine learning.
650		0	_aData protection _xLaw and legislation.
650		0	_aCryptography.
650		0	_aData encryption (Computer science).
650	1	4	_aMachine Learning.
650	2	4	_aPrivacy.
650	2	4	_aCryptology. _961576
700	1		_aZhu, Hangyu. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _91518885
700	1		_aXu, Jinjin. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _91518886
700	1		_aChen, Yang. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9880733
856	4	0	_uhttps://doi.org/10.1007/978-981-19-7083-2 _3Click Here
887			_aAkhil Chandra Saren
942			_cEBK
999			_c1609042 _d1609038