From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micro-managing the workforce as in traditional...
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ftdatacite:10.48550/arxiv.2010.09258 2023-05-15T16:02:03+02:00 From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security Shen, Sheng Zhu, Tianqing Wu, Di Wang, Wei Zhou, Wanlei 2020 https://dx.doi.org/10.48550/arxiv.2010.09258 https://arxiv.org/abs/2010.09258 unknown arXiv https://dx.doi.org/10.1002/cpe.6002 arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Distributed, Parallel, and Cluster Computing cs.DC FOS Computer and information sciences article-journal Article ScholarlyArticle Text 2020 ftdatacite https://doi.org/10.48550/arxiv.2010.09258 https://doi.org/10.1002/cpe.6002 2022-03-10T15:09:53Z Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micro-managing the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a sub-model locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multi-party computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm. : 21 pages, 4 figures, 4 tables, 76 references Article in Journal/Newspaper DML DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Distributed, Parallel, and Cluster Computing cs.DC FOS Computer and information sciences |
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Distributed, Parallel, and Cluster Computing cs.DC FOS Computer and information sciences Shen, Sheng Zhu, Tianqing Wu, Di Wang, Wei Zhou, Wanlei From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security |
topic_facet |
Distributed, Parallel, and Cluster Computing cs.DC FOS Computer and information sciences |
description |
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together rather than micro-managing the workforce as in traditional DML. One of the greatest advantages of federated learning is the additional privacy and security guarantees it affords. Federated learning architecture relies on smart devices, such as smartphones and IoT sensors, that collect and process their own data, so sensitive information never has to leave the client device. Rather, clients train a sub-model locally and send an encrypted update to the central server for aggregation into the global model. These strong privacy guarantees make federated learning an attractive choice in a world where data breaches and information theft are common and serious threats. This survey outlines the landscape and latest developments in data privacy and security for federated learning. We identify the different mechanisms used to provide privacy and security, such as differential privacy, secure multi-party computation and secure aggregation. We also survey the current attack models, identifying the areas of vulnerability and the strategies adversaries use to penetrate federated systems. The survey concludes with a discussion on the open challenges and potential directions of future work in this increasingly popular learning paradigm. : 21 pages, 4 figures, 4 tables, 76 references |
format |
Article in Journal/Newspaper |
author |
Shen, Sheng Zhu, Tianqing Wu, Di Wang, Wei Zhou, Wanlei |
author_facet |
Shen, Sheng Zhu, Tianqing Wu, Di Wang, Wei Zhou, Wanlei |
author_sort |
Shen, Sheng |
title |
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security |
title_short |
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security |
title_full |
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security |
title_fullStr |
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security |
title_full_unstemmed |
From Distributed Machine Learning To Federated Learning: In The View Of Data Privacy And Security |
title_sort |
from distributed machine learning to federated learning: in the view of data privacy and security |
publisher |
arXiv |
publishDate |
2020 |
url |
https://dx.doi.org/10.48550/arxiv.2010.09258 https://arxiv.org/abs/2010.09258 |
genre |
DML |
genre_facet |
DML |
op_relation |
https://dx.doi.org/10.1002/cpe.6002 |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2010.09258 https://doi.org/10.1002/cpe.6002 |
_version_ |
1766397677039255552 |