國立虎尾科技大學 |

Federated learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Federated learning // Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu.
作者:	Yang, Qiang,
其他作者:	Liu, Yang
面頁冊數:	1 PDF (xvii, 189 pages) :illustrations (some color). :
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Machine learning. -
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8940936
電子資源:	https://doi.org/10.2200/S00960ED2V01Y201910AIM043
ISBN:	9781681736983

Federated learning /
Yang, Qiang,1961-

Federated learning /Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu. - 1 PDF (xvii, 189 pages) :illustrations (some color). - Synthesis lectures on artificial intelligence and machine learning,#431939-4616 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 155-186).

1. Introduction -- 1.1. Motivation -- 1.2. Federated learning as a solution -- 1.3. Current development in federated learning -- 1.4. Organization of this book

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

How 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.

Mode of access: World Wide Web.

ISBN: 9781681736983

Standard No.: 10.2200/S00960ED2V01Y201910AIM043doiSubjects--Topical Terms:

561253
Machine learning.
Subjects--Index Terms:

federated learningIndex Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: Q325.5 / .Y364 2020eb

Dewey Class. No.: 006.31

Federated learning /
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020 $a 9781681736983 $q electronic
020 $z 9781687336990 $q hardcover
020 $z 9781681736976 $q paperback
024 7 $a 10.2200/S00960ED2V01Y201910AIM043 $2 doi
035 $a (CaBNVSL)mat00979860
035 $a (OCoLC)1133126613
035 $a 8940936
040 $a CaBNVSL $b eng $e rda $c CaBNVSL $d CaBNVSL
050 4 $a Q325.5 $b .Y364 2020eb
082 0 4 $a 006.31 $2 23
100 1 $a Yang, Qiang, $d 1961- $e author. $3 1253048
245 1 0 $a Federated learning / $c Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c [2020]
300 $a 1 PDF (xvii, 189 pages) : $b illustrations (some color).
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on artificial intelligence and machine learning, $x 1939-4616 ; $v #43
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 155-186).
505 0 $a 1. 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 $a 2. 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 $a 3. 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 $a 4. 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 $a 5. 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 $a 6. 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 $a 7. Incentive mechanism design for federated learning -- 7.1. Paying for contributions -- 7.2. A fairness-aware profit sharing framework -- 7.3. Discussions
505 8 $a 8. 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 $a 9. 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 $a 10. 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 $a 11. 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 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 $a How 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 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on December 23, 2019).
650 0 $a Machine learning. $3 561253
650 0 $a Federated database systems. $3 1253054
650 0 $a Data protection. $3 557764
653 $a federated learning
653 $a secure multi-party computation
653 $a privacy preserving machine learning
653 $a machine learning algorithms
653 $a transfer learning
653 $a artificial intelligence
653 $a data confidentiality
653 $a GDPR
653 $a privacy regulations
655 0 $a Electronic books. $2 local $3 554714
700 1 $a Liu, Yang $c (Ph. D. in chemical and biological engineering), $e author. $3 1253049
700 1 $a Cheng, Yong, $d 1983- $e author. $3 1253050
700 1 $a Kang, Yan $c (Ph. D. in computer science), $e author. $3 1253051
700 1 $a Chen, Tianjian, $e author. $3 1253052
700 1 $a Yu, Han $c (Ph. D. in computer science), $e author. $3 1253053
776 0 8 $i Print version: $z 9781681736976 $z 9781687336990
830 0 $a Synthesis digital library of engineering and computer science. $3 598254
830 0 $a Synthesis lectures on artificial intelligence and machine learning ; $v 29 $3 1136768
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8940936
856 4 0 $3 Abstract with links to full text $u https://doi.org/10.2200/S00960ED2V01Y201910AIM043