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Time-sensitive Learning for Heterogeneous Federated Edge Intelligence | IEEE Journals & Magazine | IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. This paper investigates real-time machine learning in a federated edge intelligence system, which implements federated learning solutions based on data samples collected from decentralized networks.
2. The paper proposes a time-sensitive federated learning framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy.
3. Solutions are proposed to address the straggler effect in TS-FL-SC and to minimize the overall time consumption of model training by selecting participating edge servers, local epoch number, and data batch size.
Article analysis:
The article is generally reliable and trustworthy as it provides an in-depth analysis of real-time machine learning in a federated edge intelligence system and proposes solutions to address the straggler effect and minimize the overall time consumption of model training. The article is well researched and provides evidence for its claims through analytical solutions, server dropping-based solutions, and joint optimization algorithms. Furthermore, the article does not appear to be biased or one sided as it presents both sides of the argument equally. However, there are some points that could be further explored such as potential risks associated with using this technology or possible counterarguments that could be presented. Additionally, more evidence could be provided for some of the claims made in order to further strengthen them.