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Code and Data Repository for An Adaptive Federated Learning Algorithm with Attenuated Memory on Non-IID and Long-tail Data

Zhenyuan Huang et al.

INFORMS Journal on Computing2026https://doi.org/10.1287/ijoc.2024.0765.cdarticle
AJG 3ABDC A
Weight
0.37

Abstract

Addressing the dual challenges of privacy protection and data sharing in sectors such as finance and healthcare, this repository proposes AFLAM (Adaptive Federated Learning Algorithm with Attenuated Memory). By leveraging the decaying mechanism of gradient history to dynamically adjust client weights, this method effectively tackles data heterogeneity and significantly enhances the overall performance of the federated learning model.

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https://doi.org/https://doi.org/10.1287/ijoc.2024.0765.cd

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@article{zhenyuan2026,
  title        = {{Code and Data Repository for An Adaptive Federated Learning Algorithm with Attenuated Memory on Non-IID and Long-tail Data}},
  author       = {Zhenyuan Huang et al.},
  journal      = {INFORMS Journal on Computing},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1287/ijoc.2024.0765.cd},
}

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Code and Data Repository for An Adaptive Federated Learning Algorithm with Attenuated Memory on Non-IID and Long-tail Data

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Evidence weight

0.37

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

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
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.