Study on attacks against federated learning
Increasingly strict data privacy laws have seen many companies that are taking advantage of big data flock over from simple collaborative learning systems to federated learning systems which promise the reservation of data privacy. However, due to the collaborative and distributed nature of federate...
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Nanyang Technological University
2021
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sg-ntu-dr.10356-1531572021-11-16T05:38:11Z Study on attacks against federated learning Wong, Yuan Neng Yeo Chai Kiat School of Computer Science and Engineering ASCKYEO@ntu.edu.sg Engineering::Computer science and engineering Increasingly strict data privacy laws have seen many companies that are taking advantage of big data flock over from simple collaborative learning systems to federated learning systems which promise the reservation of data privacy. However, due to the collaborative and distributed nature of federated learning, the resulting trained model will still be very exposed and vulnerable to many other kinds of attacks by malicious or compromised participants. In this project, we aim to study the various attack and defence methodologies that can be deployed in federated learning by implementing them using an existing open-sourced federated learning implementation as the base code. Such studies will allow us to understand the threats to the federated learning process and subsequently how to mitigate or even prevent any detrimental effects coming from the threat actors. The focus for this project will be on the distributed backdoor attack methodology and the PDGAN defence methodology. Bachelor of Engineering (Computer Science) 2021-11-09T11:42:10Z 2021-11-09T11:42:10Z 2021 Final Year Project (FYP) Wong, Y. N. (2021). Study on attacks against federated learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/153157 https://hdl.handle.net/10356/153157 en SCSE20-0797 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Wong, Yuan Neng Study on attacks against federated learning |
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Increasingly strict data privacy laws have seen many companies that are taking advantage of big data flock over from simple collaborative learning systems to federated learning systems which promise the reservation of data privacy. However, due to the collaborative and distributed nature of federated learning, the resulting trained model will still be very exposed and vulnerable to many other kinds of attacks by malicious or compromised participants. In this project, we aim to study the various attack and defence methodologies that can be deployed in federated learning by implementing them using an existing open-sourced federated learning implementation as the base code. Such studies will allow us to understand the threats to the federated learning process and subsequently how to mitigate or even prevent any detrimental effects coming from the threat actors. The focus for this project will be on the distributed backdoor attack methodology and the PDGAN defence methodology. |
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Yeo Chai Kiat |
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Yeo Chai Kiat Wong, Yuan Neng |
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Final Year Project |
author |
Wong, Yuan Neng |
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Wong, Yuan Neng |
title |
Study on attacks against federated learning |
title_short |
Study on attacks against federated learning |
title_full |
Study on attacks against federated learning |
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Study on attacks against federated learning |
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Study on attacks against federated learning |
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study on attacks against federated learning |
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Nanyang Technological University |
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2021 |
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https://hdl.handle.net/10356/153157 |
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