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DP-GenFL: a local differentially private federated learning system through generative data

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Conclusion

We have provided an enhanced privacy protection mechanism for FL. With the help of GAN, a local DP-FL framework has been proposed, and the privacy level, as well as the convergence performance, have been systemically conducted. Experimental results have shown the effectiveness and the advantages of the proposed algorithm by comparing it with the state of the art. In addition, further directions can find an optimal fake ratio and the number of training epochs for the proposed framework.

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References

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. U22A2002, 61872184, 62002170, 62071234), Fundamental Research Funds for the Central Universities (Grant No. 30921013104), Future Network Grant of Provincial Education Board in Jiangsu, Zhejiang Lab (Grant No. 2022PD0AC02), Major Science and Technology Plan of Hainan Province (Grant No. ZDKJ2021022), and Scientific Research Fund Project of Hainan University (Grant No. KYQD(ZR)-21008).

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Authors and Affiliations

  1. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Jun Li, Kang Wei & Chuan Ma

  2. Zhejiang Lab, Hangzhou, 310000, China

    Chuan Ma

  3. Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, 210096, China

    Chuan Ma

  4. School of Information and Communication Engineering, Hainan University, Haikou, 570228, China

    Feng Shu

Authors
  1. Jun Li
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  2. Kang Wei
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  3. Chuan Ma
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  4. Feng Shu
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Corresponding author

Correspondence to Chuan Ma.

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Supporting information

Appendixes A–E. The supporting information is available online at info.scichina.com and link. springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Li, J., Wei, K., Ma, C. et al. DP-GenFL: a local differentially private federated learning system through generative data. Sci. China Inf. Sci. 66, 189303 (2023). https://doi.org/10.1007/s11432-022-3678-7

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  • DOI: https://doi.org/10.1007/s11432-022-3678-7

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