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作者 Yang, Qiang, 1961- author
書名 Federated learning / Qiang Yang, Yang Liu, Yong Cheng, Yan Kang, Tianjian Chen, Han Yu
出版項 [San Rafael, California] : Morgan & Claypool, [2020]
國際標準書號 1681736985
9781681736983
國際標準號碼 10.2200/S00960ED2V01Y201910AIM043
book jacket
說明 1 online resource (1 PDF (xvii, 189 pages) :) illustrations (some color)
text rdacontent
electronic isbdmedia
online resource rdacarrier
系列 Synthesis lectures on artificial intelligence and machine learning, 1939-4616 ; #43
Synthesis digital library of engineering and computer science
Synthesis lectures on artificial intelligence and machine learning ; #43
附註 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
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
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
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
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
6. Federated transfer learning -- 6.1. Heterogeneous federated learning -- 6.2. federated transfer learning -- 6.3. The FTL framework -- 6.4. Challenges and outlook
7. Incentive mechanism design for federated learning -- 7.1. Paying for contributions -- 7.2. A fairness-aware profit sharing framework -- 7.3. Discussions
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
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
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
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
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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
Title from PDF title page (viewed on December 23, 2019)
鏈接 Print version: 9781681736976 9781687336990
主題 Machine learning
Federated database systems
Data protection
Data protection. fast (OCoLC)fst00887958
Federated database systems. fast (OCoLC)fst01763785
Machine learning. fast (OCoLC)fst01004795
federated learning
secure multi-party computation
privacy preserving machine learning
machine learning algorithms
transfer learning
artificial intelligence
data confidentiality
GDPR
privacy regulations
Electronic books
Electronic books
Alt Author Liu, Yang (Ph. D. in chemical and biological engineering), author
Cheng, Yong, 1983- author
Kang, Yan (Ph. D. in computer science), author
Chen, Tianjian, author
Yu, Han (Ph. D. in computer science), author
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