Study on attacks against federated learning
With the rise of artificial intelligence, the need for data also increases. However, many strict data privacy laws were put in place to protect personal data from being leaked. Therefore, this greatly limited the usage of artificial intelligence. Federated learning is a new form of collaborative...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/163119 |
| Summary: | With the rise of artificial intelligence, the need for data also increases. However,
many strict data privacy laws were put in place to protect personal data from
being leaked. Therefore, this greatly limited the usage of artificial intelligence.
Federated learning is a new form of collaborative machine learning that leverages
on decentralized data for training models. This introduces the possibility of being
exposed to poisoned data from malicious participants. In this project, the author
explores different attack and defence methodologies to get a better understanding
of how federated learning works. The focus is on the coordinated backdoor attack
with model-dependant triggers for attack methodology and robust learning rates
for defence methodology. The defence methodology is implemented into an opensourced federated learning base code. This will allow federated learning to be
more widely used since it is less likely to be compromised by malicious attackers
in the presence of built-in defences. |
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