Skip to main content

Advertisement

Springer Nature Link
Log in

Enhancing personalized model construction and privacy protection in federated learning with generative adversarial networks and parameter sparsification

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In the era of the Internet of Things, smart devices are widely used in industries such as healthcare, finance, and telecommunications, generating massive and diverse amounts of data. The aggregation and mining of these data can provide strong business support. Still, they also face challenges such as privacy breaches and data heterogeneity, which hinder the construction of high-quality personalized models. For this reason, this paper proposes a federated learning solution based on cloud edge architecture, combines the advantages of edge computing and cloud computing, and constructs a personalized model with a privacy protection mechanism through condition generation adversary network (cGAN) and parameter sparsity technology. Specifically, the model on the edge server is divided into two parts: using cGAN to simulate the output features of the shallow part of the model on the edge side, sparsifying the local generator and deep network, and then uploading the results to the central cloud server. By sharing edge-side generators, common knowledge is aggregated to improve local network performance while hiding shallow parts of the model. The experimental results show that this method can maintain high prediction accuracy while protecting privacy and verifying its feasibility and effectiveness in practical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. González-Sabbagh SP, Robles-Kelly A (2023) A survey on underwater computer vision. ACM Comput Surv 55(13s):1–39

    Article  MATH  Google Scholar 

  2. Yin L, Sun J, Zhou J, Gu Z, Li K (2023) Ecfa: an efficient convergent firefly algorithm for solving task scheduling problems in cloud-edge computing. IEEE Transactions on Services Computing

  3. Li J, Zhang Y, Ning J, Huang X, Poh GS, Wang D (2020) Attribute based encryption with privacy protection and accountability for cloudiot. IEEE Trans Cloud Comput 10(2):762–773

    Article  MATH  Google Scholar 

  4. Mehrish A, Majumder N, Bharadwaj R, Mihalcea R, Poria S (2023) A review of deep learning techniques for speech processing. Inf Fusion 99:101869

    Article  Google Scholar 

  5. Regulation GDP (2018) General data protection regulation (gdpr). Intersoft Consulting 24(1)

  6. Zhu J, Cao J, Saxena D, Jiang S, Ferradi H (2023) Blockchain-empowered federated learning: challenges, solutions, and future directions. ACM Comput Surv 55(11):1–31

    Article  Google Scholar 

  7. Ma Z, Ma J, Miao Y, Li Y, Deng RH (2022) Shieldfl: mitigating model poisoning attacks in privacy-preserving federated learning. IEEE Trans Inf Forensics Secur 17:1639–1654

    Article  MATH  Google Scholar 

  8. Ghimire B, Rawat DB (2022) Recent advances on federated learning for cybersecurity and cybersecurity for federated learning for internet of things. IEEE Internet Things J 9(11):8229–8249

    Article  MATH  Google Scholar 

  9. Jiang M, Wang Z, Dou Q (2022) Harmofl: harmonizing local and global drifts in federated learning on heterogeneous medical images. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 36, pp 1087–1095

  10. Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Poor HV (2021) Federated learning for internet of things: a comprehensive survey. IEEE Commun Surv Tutor 23(3):1622–1658

    Article  Google Scholar 

  11. Dwork C, Roth A, et al (2014) The algorithmic foundations of differential privacy. Found Trends® Theor Comput Sci 9(3–4): 211–407

  12. Tchaye-Kondi J, Zhai Y, Shen J, Zhu L (2023) Privacy-preserving offloading in edge intelligence systems with inductive learning and local differential privacy. IEEE Trans Netw Serv Manag 20(4):5026–5037. https://doi.org/10.1109/TNSM.2023.3266257

    Article  MATH  Google Scholar 

  13. Zhao K, Hu J, Shao H, Hu J (2023) Federated multi-source domain adversarial adaptation framework for machinery fault diagnosis with data privacy. Reliab Eng Syst Saf 236:109246

    Article  MATH  Google Scholar 

  14. Shi Y, Liu Y, Wei K, Shen L, Wang X, Tao D (2023) Make landscape flatter in differentially private federated learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 24552–24562

  15. Ma Z, Wang J, Gai K, Duan P, Zhang Y, Luo S (2023) Fully homomorphic encryption-based privacy-preserving scheme for cross edge blockchain network. J Syst Archit 134:102782

    Article  Google Scholar 

  16. Ma J, Naas S-A, Sigg S, Lyu X (2022) Privacy-preserving federated learning based on multi-key homomorphic encryption. Int J Intell Syst 37(9):5880–5901

    Article  Google Scholar 

  17. Cao X, Sun G, Yu H, Guizani M (2022) Perfed-gan: personalized federated learning via generative adversarial networks. IEEE Internet Things J 10(5):3749–3762

    Article  Google Scholar 

  18. Huang Y, Chu L, Zhou Z, Wang L, Liu J, Pei J, Zhang Y (2021) Personalized cross-silo federated learning on non-iid data. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 35, pp 7865–7873

  19. Tashakori A, Zhang W, Jane Wang Z, Servati P (2023) Semipfl: personalized semi-supervised federated learning framework for edge intelligence. IEEE Internet Things J 10(10):9161–9176. https://doi.org/10.1109/JIOT.2022.3233599

    Article  Google Scholar 

  20. Collins L, Hassani H, Mokhtari A, Shakkottai S (2021) Exploiting shared representations for personalized federated learning. In: International Conference on Machine Learning, PMLR. pp 2089–2099

  21. Sattler F, Müller K-R, Samek W (2020) Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans Neural Netw Learn Syst 32(8):3710–3722

    Article  MathSciNet  MATH  Google Scholar 

  22. Wang H, Yurochkin M, Sun Y, Papailiopoulos D, Khazaeni Y (2020) Federated learning with matched averaging. arXiv preprint arXiv:2002.06440

  23. Song M, Wang Z, Zhang Z, Song Y, Wang Q, Ren J, Qi H (2020) Analyzing user-level privacy attack against federated learning. IEEE J Sel Areas Commun 38(10):2430–2444

    Article  MATH  Google Scholar 

  24. Wang Z, Song M, Zhang Z, Song Y, Wang Q, Qi H (2019) Beyond inferring class representatives: user-level privacy leakage from federated learning. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, IEEE. pp 2512–2520

  25. Zhang L, Shen B, Barnawi A, Xi S, Kumar N, Wu Y (2021) Feddpgan: federated differentially private generative adversarial networks framework for the detection of covid-19 pneumonia. Inf Syst Front 23(6):1403–1415

    Article  MATH  Google Scholar 

  26. Xin B, Yang W, Geng Y, Chen S, Wang S, Huang L (2020) Private fl-gan: differential privacy synthetic data generation based on federated learning. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. pp 2927–2931

  27. Lin X, Liu Y, Chen F, Ge X, Huang Y (2023) Joint gradient sparsification and device scheduling for federated learning. IEEE Trans Green Commun Netw 7(3):1407–1419

    Article  MATH  Google Scholar 

  28. Wang B, Fang J, Li H, Zeng B (2023) Communication-efficient federated learning: a variance-reduced stochastic approach with adaptive sparsification. IEEE Transactions on Signal Processing

  29. Xiao H, Rasul K, Vollgraf R (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747

  30. Krizhevsky A, Hinton G (2010) Convolutional deep belief networks on cifar-10. Unpubl Manuscr 40(7): 1–9

  31. Gong B, Shi Y, Sha F, Grauman K (2012) Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, IEEE. pp 2066–2073

  32. McMahan B, Moore E, Ramage D, Hampson S, Arcas BA (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, PMLR. pp 1273–1282

  33. Li T, Sahu AK, Zaheer M, Sanjabi M, Talwalkar A, Smith V (2020) Federated optimization in heterogeneous networks. Proc Mach Learn Syst 2:429–450

    Google Scholar 

  34. Thapa C, Arachchige PCM, Camtepe S, Sun L (2022) Splitfed: when federated learning meets split learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 36, pp 8485–8493

  35. Zhu Z, Hong J, Zhou J (2021) Data-free knowledge distillation for heterogeneous federated learning. In: International Conference on Machine Learning, PMLR. pp 12878–12889

  36. Zhang C, Li S, Xia J, Wang W, Yan F, Liu Y (2020) \(\{\)BatchCrypt\(\}\): Efficient homomorphic encryption for \(\{\)Cross-Silo\(\}\) federated learning. In: 2020 USENIX Annual Technical Conference (USENIX ATC 20), pp 493–506

Download references

Acknowledgements

The authors would like to thank the anonymous reviewers for their thorough and constructive comments that have helped improve the quality of the paper. This work was supported in part by the National Natural Science Foundation of China under Grant 62271096 and Grant U20A20157; in part by the Natural Science Foundation of Chongqing of China under Grant CSTB2023NSCQLZX0134; and in part by the Doctoral Innovation Talent Program of Chongqing University of Posts and Telecommunications under Grant BYJS202203.

Author information

Authors and Affiliations

  1. School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Zhongyuan Jing & Ruyan Wang

  2. The Advanced Network and Intelligent Connection Technology Key Laboratory, Chongqing, 400065, China

    Zhongyuan Jing & Ruyan Wang

  3. Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China

    Zhongyuan Jing & Ruyan Wang

Authors
  1. Zhongyuan Jing
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ruyan Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhongyuan Jing.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jing, Z., Wang, R. Enhancing personalized model construction and privacy protection in federated learning with generative adversarial networks and parameter sparsification. J Supercomput 81, 561 (2025). https://doi.org/10.1007/s11227-025-07038-8

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-025-07038-8

Keywords