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Article analysis:

The article “How To Backdoor Federated Learning” provides a detailed overview of how attackers can introduce hidden backdoor functionality into federated learning models. The article is well written and provides a comprehensive explanation of the attack methodology, as well as its evaluation under different assumptions for standard federated-learning tasks. However, there are some potential biases in the article that should be noted. For example, the article does not explore any counterarguments or present both sides of the issue equally; instead it focuses solely on how attackers can exploit federated learning models for malicious purposes. Additionally, there is no discussion of possible risks associated with this type of attack or any mention of potential solutions to mitigate these risks. Furthermore, while the article does provide evidence for its claims, it could benefit from additional evidence and research to further support its conclusions. In conclusion, while this article provides a thorough overview of how attackers can exploit federated learning models for malicious purposes, it could benefit from more balanced reporting and additional evidence to further support its claims.