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Article summary:
1. This article proposes a new model poisoning attack to federated learning called MPAF, which is based on fake clients.
2. The attack works by injecting fake clients into the federated learning system and sending carefully crafted fake local model updates to the cloud server during training.
3. Experiments show that MPAF can significantly decrease the test accuracy of the global model, even if classical defenses and norm clipping are adopted.
Article analysis:
The article is generally trustworthy and reliable in its presentation of the proposed Model Poisoning Attack based on Fake Clients (MPAF). The authors provide a clear explanation of how the attack works and present evidence from experiments that demonstrate its effectiveness in decreasing the test accuracy of the global model. There are no obvious biases or unsupported claims in the article, and all points are well-supported with evidence from experiments. The authors also note potential risks associated with their proposed attack, such as decreased accuracy for many indiscriminate test inputs, highlighting their awareness of possible implications of their work. In general, this article provides an unbiased overview of MPAF and presents both sides equally without any promotional content or partiality.