000			06329nam a2200865 i 4500
001			8940936
003			IEEE
005			20200413152931.0
006			m eo d
007			cr cn |||m|||a
008			191223s2020 caua fob 000 0 eng d
020			_a9781681736983 _qelectronic
020			_z9781687336990 _qhardcover
020			_z9781681736976 _qpaperback
024	7		_a10.2200/S00960ED2V01Y201910AIM043 _2doi
035			_a(CaBNVSL)mat00979860
035			_a(OCoLC)1133126613
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ325.5 _b.Y364 2020eb
082	0	4	_a006.31 _223
100	1		_aYang, Qiang, _d1961- _eauthor.
245	1	0	_aFederated learning / _cQiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu.
264		1	_a[San Rafael, California] : _bMorgan & Claypool, _c[2020]
300			_a1 PDF (xvii, 189 pages) : _billustrations (some color).
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aSynthesis lectures on artificial intelligence and machine learning, _x1939-4616 ; _v#43
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
500			_aPart of: Synthesis digital library of engineering and computer science.
504			_aIncludes bibliographical references (pages 155-186).
505	0		_a1. Introduction -- 1.1. Motivation -- 1.2. Federated learning as a solution -- 1.3. Current development in federated learning -- 1.4. Organization of this book
505	8		_a2. Background -- 2.1. Privacy-preserving machine learning -- 2.2. PPML and secure ML -- 2.3. Threat and security models -- 2.4. Privacy preservation techniques
505	8		_a3. Distributed machine learning -- 3.1. Introduction to DML -- 3.2. Scalability-motivated DML -- 3.3. Privacy-motivated DML -- 3.4. Privacy-preserving gradient descent -- 3.5. Summary
505	8		_a4. Horizontal federated learning -- 4.1. The definition of HFL -- 4.2. Architecture of HFL -- 4.3. The federated averaging algorithm -- 4.4. improvement of the FedAvg algorithm -- 4.5. Related works -- 4.6. Challenges and outlook
505	8		_a5. Vertical federated learning -- 5.1. The definition of VFL -- 5.2. Architecture of VFL -- 5.3. Algorithms of VFL -- 5.4. Challenges and outlook
505	8		_a6. Federated transfer learning -- 6.1. Heterogeneous federated learning -- 6.2. federated transfer learning -- 6.3. The FTL framework -- 6.4. Challenges and outlook
505	8		_a7. Incentive mechanism design for federated learning -- 7.1. Paying for contributions -- 7.2. A fairness-aware profit sharing framework -- 7.3. Discussions
505	8		_a8. Federated learning for vision, language, and recommendation -- 8.1. Federated learning for computer vision -- 8.2. Federated Learning for NLP -- 8.3. Federated learning for recommendation systems
505	8		_a9. Federated reinforcement learning -- 9.1. Introduction to reinforcement learning -- 9.2. Reinforcement learning algorithms -- 9.3. Distributed reinforcement learning -- 9.4. Federated reinforcement learning -- 9.5. Challenges and outlook
505	8		_a10. Selected applications -- 10.1. Finance -- 10.2. Healthcare -- 10.3. Education -- 10.4. Urban computing and smart city -- 10.5. Edge computing and internet of things -- 10.6. Blockchain -- 10.7. 5G mobile networks
505	8		_a11. Summary and outlook -- A. Legal development on data protection -- A.1. Data protection in the European Union -- A.2. Data protection in the USA -- A.3. Data protection in China.
506			_aAbstract freely available; full-text restricted to subscribers or individual document purchasers.
510	0		_aCompendex
510	0		_aINSPEC
510	0		_aGoogle scholar
510	0		_aGoogle book search
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.
530			_aAlso available in print.
588			_aTitle from PDF title page (viewed on December 23, 2019).
650		0	_aMachine learning.
650		0	_aFederated database systems.
650		0	_aData protection.
653			_afederated learning
653			_asecure multi-party computation
653			_aprivacy preserving machine learning
653			_amachine learning algorithms
653			_atransfer learning
653			_aartificial intelligence
653			_adata confidentiality
653			_aGDPR
653			_aprivacy regulations
655		0	_aElectronic books.
700	1		_aLiu, Yang _c(Ph. D. in chemical and biological engineering), _eauthor.
700	1		_aCheng, Yong, _d1983- _eauthor.
700	1		_aKang, Yan _c(Ph. D. in computer science), _eauthor.
700	1		_aChen, Tianjian, _eauthor.
700	1		_aYu, Han _c(Ph. D. in computer science), _eauthor.
776	0	8	_iPrint version: _z9781681736976 _z9781687336990
830		0	_aSynthesis digital library of engineering and computer science.
830		0	_aSynthesis lectures on artificial intelligence and machine learning ; _v#43.
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8940936
856	4	0	_3Abstract with links to full text _uhttps://doi.org/10.2200/S00960ED2V01Y201910AIM043
999			_c562404 _d562404