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Advances and open problems in federated learning
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(2021) Advances and open problems in federated learning. Foundations and Trends® in Machine Learning, 14 (1–2). pp. 1-210. doi:10.1561/2200000083 ISSN 1935-8237.
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Official URL: http://dx.doi.org/10.1561/2200000083
Abstract
Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this monograph discusses recent advances and presents an extensive collection of open problems and challenges.
Item Type: | Journal Article | ||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Journal or Publication Title: | Foundations and Trends® in Machine Learning | ||||||
Publisher: | Now Publishers Inc. | ||||||
ISBN: | 9781680837889 | ||||||
ISSN: | 1935-8237 | ||||||
Official Date: | 23 June 2021 | ||||||
Dates: |
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Volume: | 14 | ||||||
Number: | 1–2 | ||||||
Page Range: | pp. 1-210 | ||||||
DOI: | 10.1561/2200000083 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | The final publication is available from now publishers via http://dx.doi.org/10.1561/2200000083. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Date of first compliant deposit: | 18 October 2022 | ||||||
Date of first compliant Open Access: | 19 October 2022 |
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