Towards practical data alignment in production federated learning

doi:10.1007/s11704-024-3936-0

Front. Comput. Sci.

2025, Vol. 19

Issue (1) : 191603 https://doi.org/10.1007/s11704-024-3936-0

Information Systems

Towards practical data alignment in production federated learning

Yexuan SHI^1,², Wei YU³, Yuanyuan ZHANG¹(

), Chunbo XUE^1,², Yuxiang ZENG¹, Zimu ZHOU⁴, Manxue GUO³, Lun XIN³, Wenjing NIE³

¹. State Key Laboratory of Complex & Critical Software Environment and Advanced Innovation Center for Future Blockchain and Privacy Computing, Beihang University, Beijing 100191, China
². Zhongguancun Pan Connected Mobile Communication Technology Innovation and Application Research Institute,Beijing 100088, China
³. China Mobile Research Institute, Beijing 100053, China
⁴. School of Data Science, City University of Hong Kong, Hong Kong 999077, China

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Corresponding Author(s): Yuanyuan ZHANG

Just Accepted Date: 27 June 2024 Issue Date: 23 August 2024

Cite this article:

Yexuan SHI,Wei YU,Yuanyuan ZHANG, et al. Towards practical data alignment in production federated learning[J]. Front. Comput. Sci., 2025, 19(1): 191603.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-3936-0
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I1/191603

Fig.1 Overview of

α

-ESF framework

Fig.2 Experimental results of varying

α

on real datasets. (a) Running time; (b) communication cost

Fig.3 Model performance with data alignment solution

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