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Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning

Overview of attention for article published in IEEE Transactions on Big Data, October 2023
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About this Attention Score

  • Among the highest-scoring outputs from this source (#50 of 278)
  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

patent
2 patents

Readers on

mendeley
1 Mendeley
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Title
Mix2SFL: Two-Way Mixup for Scalable, Accurate, and Communication-Efficient Split Federated Learning
Published in
IEEE Transactions on Big Data, October 2023
DOI 10.1109/tbdata.2023.3328424
Authors

Seungeun Oh, Hyelin Nam, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Seong-Lyun Kim

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Computer Science 1 100%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 05 December 2023.
All research outputs
#8,543,833
of 25,394,764 outputs
Outputs from IEEE Transactions on Big Data
#50
of 278 outputs
Outputs of similar age
#122,312
of 357,651 outputs
Outputs of similar age from IEEE Transactions on Big Data
#1
of 5 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 278 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done well, scoring higher than 75% 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 357,651 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 5 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