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[2301.05795] Poisoning Attacks and Defenses in Federated Learning: A Survey
Source: arxiv.53yu.com
Article summary:
1. This article provides a survey of poisoning attacks and defenses in federated learning.
2. It includes bibliographic and citation tools, code, data, media, demos, and related papers associated with the article.
3. It also introduces arXivLabs as a framework for collaborators to develop and share new arXiv features directly on the website.
Article analysis:
The trustworthiness and reliability of this article is generally high due to its comprehensive coverage of poisoning attacks and defenses in federated learning. The article provides detailed information on bibliographic and citation tools, code, data, media, demos, and related papers associated with the article which can be used to further research into this topic. Additionally, it introduces arXivLabs as a framework for collaborators to develop and share new arXiv features directly on the website which can help facilitate collaboration between researchers in this field.
The only potential bias that could be present in this article is that it may not present both sides of an argument equally or explore counterarguments thoroughly enough. However, since this is a survey paper rather than an opinion piece or argumentative essay there is no need for such exploration so this potential bias does not affect the trustworthiness or reliability of the article significantly.