Federated Learning (Record no. 85292)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04033nam a22005895i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-01585-4 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730164106.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2020 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031015854 |
| -- | 978-3-031-01585-4 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 100 1# - AUTHOR NAME | |
| Author | Yang, Qiang. |
| 245 10 - TITLE STATEMENT | |
| Title | Federated Learning |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XVII, 189 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Artificial Intelligence and Machine Learning, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface -- Acknowledgments -- Introduction -- Background -- Distributed Machine Learning -- Horizontal Federated Learning -- Vertical Federated Learning -- Federated Transfer Learning -- Incentive Mechanism Design for Federated Learning -- Federated Learning for Vision, Language, and Recommendation -- Federated Reinforcement Learning -- Selected Applications -- Summary and Outlook -- Bibliography -- Authors' Biographies. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Liu, Yang. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Cheng, Yong. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Kang, Yan. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Chen, Tianjian. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Yu, Han. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01585-4 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2020. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) . |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical Models of Cognitive Processes and Neural Networks. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1939-4616 |
| 912 ## - | |
| -- | ZDB-2-SXSC |
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