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Code and Data Repository for A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring

Yidong Chai et al.

INFORMS Journal on Computing2026https://doi.org/10.1287/ijoc.2023.0521.cdarticle
AJG 3ABDC A
Weight
0.50

What the paper says

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring by Yidong Chai, Haoxin Liu, Xiao Liu, Liuan Wang, Yi Liu, Junqu Wang.

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Cite this paper

https://doi.org/https://doi.org/10.1287/ijoc.2023.0521.cd

Or copy a formatted citation

@article{yidong2026,
  title        = {{Code and Data Repository for A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring}},
  author       = {Yidong Chai et al.},
  journal      = {INFORMS Journal on Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/ijoc.2023.0521.cd},
}

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Code and Data Repository for A Novel Personalized Federated Learning Method for Privacy-Preserving Smart Mobile Health Monitoring

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.