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Article summary:
1. This paper proposes a novel communication-efficient federated edge learning (FEEL) framework via optimal probabilistic device scheduling.
2. The proposed framework is based on a tight bound to approximate the remaining communication time through cross-disciplinary effort that combines the learning theory for convergence rate analysis and communication theory for per-round latency analysis.
3. Extensive experiments are provided to verify the superiority of the proposed policy over three benchmark policies based on indirect solution approaches.
Article analysis:
The article “Communication-Efficient Federated Edge Learning via Optimal Probabilistic Device Scheduling” is an informative and well-written piece of research that provides a comprehensive overview of the current state of federated edge learning (FEEL). The authors provide a detailed description of their proposed framework, which is based on a tight bound to approximate the remaining communication time through cross-disciplinary effort that combines the learning theory for convergence rate analysis and communication theory for per-round latency analysis. Furthermore, they provide extensive experiments to verify the superiority of their proposed policy over three benchmark policies based on indirect solution approaches.
The article appears to be unbiased and presents both sides equally, as it provides an in-depth discussion of both existing solutions and their own proposed approach. Additionally, all claims made in the article are supported by evidence from real world datasets and use cases, such as collaborative 3D object detection in autonomous driving. Furthermore, potential risks associated with FEEL are noted throughout the article, such as limited radio resources causing severe congestion over air interface due to uncertain wireless environment.
In conclusion, this article is reliable and trustworthy due to its comprehensive coverage of FEEL topics and its unbiased presentation of both existing solutions and their own proposed approach.