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Article summary:
1. Federated learning (FL) has emerged in edge computing to address the limited bandwidth and privacy concerns of traditional cloud-based training.
2. FedCH is proposed as an efficient FL mechanism to accelerate FL in heterogeneous edge computing, by constructing a special cluster topology and performing hierarchical aggregation for training.
3. Extensive experiments show that FedCH reduces the completion time and network traffic compared with existing FL mechanisms.
Article analysis:
The article is generally reliable and trustworthy, as it provides detailed information about the proposed mechanism, FedCH, which is designed to accelerate federated learning in heterogeneous edge computing environments. The article also provides evidence from extensive experiments conducted on both physical platforms and simulated environments to support its claims. However, there are some potential biases that should be noted. For example, the article does not explore any counterarguments or alternative solutions to the problem of accelerating federated learning in heterogeneous edge computing environments. Additionally, the article does not discuss any possible risks associated with using this proposed mechanism or provide any insight into how these risks can be mitigated. Furthermore, while the article does present both sides of the argument equally, it does not provide enough detail about each side for readers to make an informed decision about which solution is best suited for their needs.