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Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation

Overview of attention for article published in IEEE Transactions on Neural Networks and Learning Systems, December 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

twitter
6 X users
patent
17 patents

Readers on

mendeley
293 Mendeley
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Article details
Title
Communication-Efficient Federated Deep Learning With Layerwise Asynchronous Model Update and Temporally Weighted Aggregation
Published in
IEEE Transactions on Neural Networks and Learning Systems, December 2019
DOI 10.1109/tnnls.2019.2953131
Pubmed ID
Authors

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Timeline Attention over time Attention Score history
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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 293 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 293 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 52 18%
Student > Master 30 10%
Researcher 19 6%
Student > Bachelor 13 4%
Student > Doctoral Student 10 3%
Other 25 9%
Unknown 144 49%
Readers by discipline
Readers by discipline Count As %
Computer Science 93 32%
Engineering 24 8%
Social Sciences 4 1%
Arts and Humanities 3 1%
Mathematics 3 1%
Other 15 5%
Unknown 151 52%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 April 2026.
All research outputs
#2,629,453
of 32,452,905 outputs
Outputs from IEEE Transactions on Neural Networks and Learning Systems
#57
of 2,940 outputs
Outputs of similar age
#51,091
of 511,285 outputs
Outputs of similar age from IEEE Transactions on Neural Networks and Learning Systems
#1
of 8 outputs
Altmetric has tracked 32,452,905 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,940 research outputs from this source. They receive a mean Attention Score of 2.9. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 511,285 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them