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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
[San Rafael, California]
Morgan & Claypool
[2020]
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| Series: | Synthesis lectures on artificial intelligence and machine learning
#43 |
| Subjects: | |
| Links: | https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6001573 https://doi.org/10.2200/S00960ED2V01Y201910AIM043 |
| 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 |
| Item Description: | Title from PDF title page (viewed on December 23, 2019) |
| Physical Description: | 1 Online-Ressource Illustrationen |
| ISBN: | 9781681736983 |
| DOI: | 10.2200/S00960ED2V01Y201910AIM043 |
Staff View
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| id | DE-604.BV046427689 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:51:57Z |
| institution | BVB |
| isbn | 9781681736983 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031839991 |
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| publishDate | 2020 |
| publishDateSearch | 2020 |
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| publisher | Morgan & Claypool |
| record_format | marc |
| series | Synthesis lectures on artificial intelligence and machine learning |
| series2 | Synthesis lectures on artificial intelligence and machine learning |
| 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 |