ML Anthology

FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update

Ji Liu, Juncheng Jia, Tianshi Che, Chao Huo, Jiaxiang Ren, Yang Zhou, Huaiyu Dai, Dejing Dou
AAAI 2024 pp. 13900-13908
doi:10.1609/AAAI.V38I12.29297 /aaai/2024/liu2024aaai-fedasmu/
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Cite

Text

Liu et al. "FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I12.29297

Markdown

[Liu et al. "FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/liu2024aaai-fedasmu/) doi:10.1609/AAAI.V38I12.29297

BibTeX

@inproceedings{liu2024aaai-fedasmu,
  title     = {{FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update}},
  author    = {Liu, Ji and Jia, Juncheng and Che, Tianshi and Huo, Chao and Ren, Jiaxiang and Zhou, Yang and Dai, Huaiyu and Dou, Dejing},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {13900-13908},
  doi       = {10.1609/AAAI.V38I12.29297},
  url       = {https://mlanthology.org/aaai/2024/liu2024aaai-fedasmu/}
}