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Article summary:
1. This paper investigates the impact of communication factors on the convergence performance of federated learning (FL) in wireless networks.
2. A joint resource allocation and user scheduling scheme is proposed to minimize the maximum update delay of user training.
3. Simulation results show that the convergence time can be reduced by 41.6% compared with the random scheduling allocation scheme.
Article analysis:
The article provides a comprehensive overview of federated learning (FL) in wireless networks, and presents a joint resource allocation and user scheduling scheme to minimize the maximum update delay of user training. The article is well-structured and provides detailed information about related works, which makes it reliable and trustworthy. However, there are some potential biases that should be noted. For example, the article does not provide any evidence for its claims or explore counterarguments, which could lead to one-sided reporting or partiality in favor of its proposed solution. Additionally, possible risks associated with FL are not discussed in detail, which could lead to an incomplete understanding of this technology. Furthermore, while the article mentions some related works, it does not present both sides equally or explore all possible solutions for optimizing resource allocation and user scheduling in FL networks. In conclusion, while this article is generally reliable and trustworthy, there are some potential biases that should be taken into consideration when reading it.