A defense method against multi-label poisoning attacks in federated learning
Abstract Federated learning is a distributed machine learning framework that allows multiple parties to collaboratively train models without sharing raw data. While it enhances data privacy, it is vulnerable to malicious attacks, especially data poisoning attacks like label flipping. Traditional def...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-09672-x |
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