Federated learning:
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 la...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
[San Rafael, California]
Morgan & Claypool
[2020]
|
| Schriftenreihe: | Synthesis lectures on artificial intelligence and machine learning
#43 |
| Schlagworte: | |
| Online-Zugang: | TUM01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Title from PDF title page (viewed on December 23, 2019) |
| Beschreibung: | 1 Online-Ressource Illustrationen |
| ISBN: | 9781681736983 |
| DOI: | 10.2200/S00960ED2V01Y201910AIM043 |
Internformat
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| 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 | ||
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Federated database systems | |
| 650 | 4 | |a Data protection | |
| 700 | 1 | |a Liu, Yang |e Sonstige |4 oth | |
| 700 | 1 | |a Cheng, Yong |e Sonstige |4 oth | |
| 700 | 1 | |a Kang, Yan |e Sonstige |4 oth | |
| 700 | 1 | |a Chen, Tianjian |e Sonstige |4 oth | |
| 700 | 1 | |a Yu, Han |e Sonstige |4 oth | |
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Datensatz im Suchindex
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| author | Yang, Qiang 1961- |
| author_GND | (DE-588)135614120 |
| author_facet | Yang, Qiang 1961- |
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| author_sort | Yang, Qiang 1961- |
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| dewey-full | 006.31 |
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| dewey-ones | 006 - Special computer methods |
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| dewey-sort | 16.31 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.2200/S00960ED2V01Y201910AIM043 |
| format | Electronic eBook |
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| indexdate | 2024-07-10T08:44:19Z |
| institution | BVB |
| isbn | 9781681736983 |
| language | English |
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| series | Synthesis lectures on artificial intelligence and machine learning |
| series2 | Synthesis lectures on artificial intelligence and machine learning |
| spelling | Yang, Qiang 1961- Verfasser (DE-588)135614120 aut Federated learning Qiang Yang (WeBank and Hong Kong University of Science and Technology), Yang Liu (WeBank, China), Yong Cheng (WeBank, China), Yan Kang (WeBank, China), Tianjian Chen (WeBank, China), Han Yu (Nanyang Technological University, Singapore) [San Rafael, California] Morgan & Claypool [2020] 1 Online-Ressource Illustrationen txt rdacontent c rdamedia cr rdacarrier Synthesis lectures on artificial intelligence and machine learning #43 Title from PDF title page (viewed on December 23, 2019) 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 Machine learning Federated database systems Data protection Liu, Yang Sonstige oth Cheng, Yong Sonstige oth Kang, Yan Sonstige oth Chen, Tianjian Sonstige oth Yu, Han Sonstige oth Erscheint auch als Druck-Ausgabe, Paperback 978-1-68173-697-6 Erscheint auch als Druck-Ausgabe, Hardcover 978-1-68173-699-0 Synthesis lectures on artificial intelligence and machine learning #43 (DE-604)BV043983076 43 https://doi.org/10.2200/S00960ED2V01Y201910AIM043 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Yang, Qiang 1961- Federated learning Synthesis lectures on artificial intelligence and machine learning Machine learning Federated database systems Data protection |
| title | Federated learning |
| title_auth | Federated learning |
| title_exact_search | Federated learning |
| title_full | Federated learning Qiang Yang (WeBank and Hong Kong University of Science and Technology), Yang Liu (WeBank, China), Yong Cheng (WeBank, China), Yan Kang (WeBank, China), Tianjian Chen (WeBank, China), Han Yu (Nanyang Technological University, Singapore) |
| title_fullStr | Federated learning Qiang Yang (WeBank and Hong Kong University of Science and Technology), Yang Liu (WeBank, China), Yong Cheng (WeBank, China), Yan Kang (WeBank, China), Tianjian Chen (WeBank, China), Han Yu (Nanyang Technological University, Singapore) |
| title_full_unstemmed | Federated learning Qiang Yang (WeBank and Hong Kong University of Science and Technology), Yang Liu (WeBank, China), Yong Cheng (WeBank, China), Yan Kang (WeBank, China), Tianjian Chen (WeBank, China), Han Yu (Nanyang Technological University, Singapore) |
| title_short | Federated learning |
| title_sort | federated learning |
| topic | Machine learning Federated database systems Data protection |
| topic_facet | Machine learning Federated database systems Data protection |
| url | https://doi.org/10.2200/S00960ED2V01Y201910AIM043 |
| volume_link | (DE-604)BV043983076 |
| work_keys_str_mv | AT yangqiang federatedlearning AT liuyang federatedlearning AT chengyong federatedlearning AT kangyan federatedlearning AT chentianjian federatedlearning AT yuhan federatedlearning |