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Towards practical data alignment in production federated learning

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. U21A20516, 62076017, and 6233000216), the Beihang University Basic Research Funding (No. YWF-22-L-531), and the CCF-Huawei Populus Grove Fund (CCF-HuaweiDB202310).

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Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Complex & Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing, 100191, China

    Yexuan Shi, Yuanyuan Zhang, Chunbo Xue & Yuxiang Zeng

  2. Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute, Beijing, 100088, China

    Yexuan Shi & Chunbo Xue

  3. China Mobile Research Institute, Beijing, 100053, China

    Wei Yu, Manxue Guo, Lun Xin & Wenjing Nie

  4. School of Data Science, City University of Hong Kong, Hong Kong, 999077, China

    Zimu Zhou

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  1. Yexuan Shi
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  2. Wei Yu
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  3. Yuanyuan Zhang
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  9. Wenjing Nie
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Corresponding author

Correspondence to Yuanyuan Zhang.

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Shi, Y., Yu, W., Zhang, Y. et al. Towards practical data alignment in production federated learning. Front. Comput. Sci. 19, 191603 (2025). https://doi.org/10.1007/s11704-024-3936-0

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  • DOI: https://doi.org/10.1007/s11704-024-3936-0

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