How To Backdoor Federated Learning¶
Bagdasaryan, E. (2020). How To Backdoor Federated Learning. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics.
Cited by¶
1 citation across 1 artifact.
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Domain-specific¶
- Data Poisoning Attack
- Federated-learning poisoning has a participant client submit malicious gradient updates to the aggregation server, corrupting the jointly-trained model without exposing the poisoning data to any central auditor
This sourceModel-replacement poisoning in federated learning, in which malicious participants submit crafted updates that backdoor the jointly trained model while secure aggregation prevents anyone from auditing their data or updates.
SupportedVerified against the source
- Federated-learning poisoning has a participant client submit malicious gradient updates to the aggregation server, corrupting the jointly-trained model without exposing the poisoning data to any central auditor
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