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Poisoning Attacks in Federated Learning: A Survey | IEEE Journals & Magazine | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This article provides an overview of poisoning attacks and their defense strategies in federated learning.
2. It classifies the poisoning attacks according to their methods and targets, as well as the defense strategies against them into three categories.
3. It also discusses the privacy protection problem in poisoning attacks and their countermeasures, as well as potential research directions from the perspective of attack and defense.
Article analysis:
The article is a comprehensive survey of poisoning attacks and their defense strategies in federated learning, providing an up-to-date overview of the topic. The article is well-structured, with clear sections that cover different aspects of the topic. The authors provide a thorough analysis of the differences between various categories of poisoning attacks, as well as a detailed discussion on privacy protection issues related to these attacks and their countermeasures.
The article appears to be unbiased and objective, presenting both sides equally without any promotional content or partiality. The authors have provided evidence for all claims made in the article, such as citing relevant laws (e.g., HIPAA and GDPR) to support their argument about data privacy concerns when sharing data among multiple enterprises for training models. Furthermore, they have explored possible counterarguments by discussing potential risks associated with federated learning (e.g., poisoning attacks).
In conclusion, this article is reliable and trustworthy due to its comprehensive coverage of the topic, unbiased presentation of both sides equally, evidence-based claims, exploration of counterarguments, and lack of promotional content or partiality.