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Federated learning /

By: Yang, Qiang 1961- [author.].
Contributor(s): 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.].
Material type: materialTypeLabelBookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on artificial intelligence and machine learning: #43.Publisher: [San Rafael, California] : Morgan & Claypool, [2020]Description: 1 PDF (xvii, 189 pages) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681736983.Subject(s): Machine learning | Federated database systems | Data protection | federated learning | secure multi-party computation | privacy preserving machine learning | machine learning algorithms | transfer learning | artificial intelligence | data confidentiality | GDPR | privacy regulationsGenre/Form: Electronic books.DDC classification: 006.31 Online resources: Abstract with links to resource | Abstract with links to full text Also available in print.
Contents:
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.
Summary: 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.
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Item type Current location Call number Status Date due Barcode Item holds
E books E books PK Kelkar Library, IIT Kanpur
Available EBKE904
Total holds: 0

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

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.

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

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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.

Also available in print.

Title from PDF title page (viewed on December 23, 2019).

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