Skip to main content

Advertisement

SpringerLink
Log in

Swarm Learning-based Secure and Fair Model Sharing for Metaverse Healthcare

  • Research
  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

Metaverse is a digital space that aims to build a fully immersive, hyper spatio-temporal virtual world for human interaction. It is very promising to apply metaverse into personal healthcare to improve the quality and efficiency of personal healthcare services. Several challenges, such as patient data security and privacy issues, are hindering the application of metaverse healthcare. To protect data security and privacy of healthcare, swarm learning is proposed by integrating distributed machine learning and blockchain. However, the existing swarm learning framework often faces additional issues of security and fairness in metaverse healthcare, such as security concerns caused by multiple anonymous avatars and uneven distribution of data quality. In this article, we propose a swarm learning-based model sharing framework to enhance the security and fairness of healthcare-AI model sharing in the metaverse. The proposed metaverse swarm learning can support the privacy-protection global model and partial model parameters merging. Moreover, the decentralized autonomous organization blockchain network is proposed to guarantee the fairness of model sharing gains among the imbalance of healthcare resource data. Simulation results on two practical healthcare datasets show that our proposed model-sharing can achieve better accuracy than local training and approximate accuracy compared to central training.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

Data Availability

The COVID-19 dataset that support the research of this study are openly available in: https://data.mendeley.com/datasets/9xkhgts2s6/1. And the PAMAP dataset that support the research of this study are openly available via the UCI Machine Learning Repository at the following identifier: https://doi.org/10.24432/C5NW2H

References

  1. George AH, Fernando M, George AS, Baskar T, Pandey D (2021) Metaverse: The next stage of human culture and the internet. Int j adv res trends eng technol 8(12):1–10

    Google Scholar 

  2. Joshua J (2017) Information bodies: computational anxiety in neal stephenson’s snow crash. Interdiscip Lit Stud 19(1):17–47

    Article  Google Scholar 

  3. Kang J, Ye D, Nie, J., Xiao, J., Deng, X., Wang, S., Xiong, Z., Yu, R., Niyato, D.: Blockchain-based federated learning for industrial metaverses: Incentive scheme with optimal aoi. In: 2022 IEEE International Conference on Blockchain (Blockchain), pp. 71–78 (2022). IEEE

  4. Deveci M, Mishra AR, Gokasar I, Rani P, Pamucar D, Ozcan E (2022) A decision support system for assessing and prioritizing sustainable urban transportation in metaverse. IEEE Transactions on Fuzzy Systems

  5. Thomason J (2021) Metahealth-how will the metaverse change health care? Journal of Metaverse 1(1):13–16

    Google Scholar 

  6. Urbankova A, Eber M, Engebretson SP (2013) A complex haptic exercise to predict preclinical operative dentistry performance: a retrospective study. J Dent Educ 77(11):1443–1450

    Article  Google Scholar 

  7. Almousa O, Prates J, Yeslam N, Mac Gregor D, Zhang J, Phan V, Nielsen M, Smith R, Qayumi K (2019) Virtual reality simulation technology for cardiopulmonary resuscitation training: An innovative hybrid system with haptic feedback. Simulation & Gaming 50(1):6–22

    Article  Google Scholar 

  8. Sridhar A, Shiliang Z, Woodson R, Kwan L (2020) Non-pharmacological anxiety reduction with immersive virtual reality for first-trimester dilation and curettage: a pilot study. Eur J Contracept Reprod Health Care 25(6):480–483

    Article  Google Scholar 

  9. Scolozzi P, Bijlenga P (2017) Removal of recurrent intraorbital tumour using a system of augmented reality. Br J Oral Maxillofac Surg 55(9):962–964

    Article  Google Scholar 

  10. Cheng JZ, Ni D, Chou YH, Qin J, Tiu CM, Chang YC, Huang CS, Shen D, Chen CM (2016) Computer-aided diagnosis with deep learning architecture: applications to breast lesions in us images and pulmonary nodules in ct scans. Scientific reports 6(1):1–13

    Google Scholar 

  11. Tekkeşin Aİ et al (2019) Artificial intelligence in healthcare: past, present and future. Anatol J Cardiol 22(Suppl 2):8–9

    Google Scholar 

  12. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs, T.J.: Clinicalgrade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine 25(8), 1301–1309 (2019)

  13. 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  Google Scholar 

  14. Li J, Shao Y, Wei K, Ding M, Ma C, Shi L, Han Z, Poor HV (2021) Blockchain assisted decentralized federated learning (blade-fl): Performance analysis and resource allocation. IEEE Trans Parallel Distrib Syst 33(10):2401–2415

    Article  Google Scholar 

  15. Warnat-Herresthal S, Schultze H, Shastry KL, Manamohan S, Mukherjee S, Garg V, Sarveswara R, Händler K, Pickkers P, Aziz NA et al (2021) Swarm learning for decentralized and confidential clinical machine learning. Nature 594(7862):265–270

    Article  Google Scholar 

  16. Saldanha OL, Quirke P, West NP, James JA, Loughrey MB, Grabsch HI, Salto-Tellez M, Alwers E, Cifci D, Ghaffari Laleh N et al (2022) Swarm learning for decentralized artificial intelligence in cancer histopathology. Nature Medicine 28(6):1232–1239

    Article  Google Scholar 

  17. Nakamoto S (2008) Bitcoin: A peer-to-peer electronic cash system. Decentralized business review, 21260

  18. Szabo N (1997) Formalizing and securing relationships on public networks. First monday

  19. Wood G et al (2014) Ethereum: A secure decentralised generalised transaction ledger. Ethereum project yellow paper 151(2014):1-32

    Google Scholar 

  20. Wang S, Ding W, Li J, Yuan Y, Ouyang L, Wang FY (2019) Decentralized autonomous organizations: Concept, model, and applications. IEEE Transactions on Computational Social Systems 6(5):870–878

    Article  Google Scholar 

  21. Wang FY, Ding W, Wang X, Garibaldi J, Teng S, Imre R, Olaverri-Monreal C (2022) The dao to desci: Ai for free, fair, and responsibility sensitive sciences. IEEE Intelligent Systems 37(2):16–22

    Article  Google Scholar 

  22. Li J, Qin R, Wang FY (2022) The future of management: Dao to smart organizations and intelligent operations. IEEE Transactions on Systems, Man, and Cybernetics: Systems

  23. Lalitha A, Shekhar S, Javidi T, Koushanfar F (2018) Fully decentralized federated learning. In: ThirdWorkshop on Bayesian Deep Learning (NeurIPS), vol. 2

  24. Lu S, Zhang Y, Wang Y (2020) Decentralized federated learning for electronic health records. In: 2020 54th Annual Conference on Information Sciences and Systems (CISS), pp. 1–5. IEEE

  25. Ramanan P, Nakayama K (2020) Baffle: Blockchain based aggregator free federated learning. In: 2020 IEEE International Conference on Blockchain (Blockchain), pp.72–81. IEEE

  26. Nguyen DC, Ding M, Pham QV, Pathirana PN, Le LB, Seneviratne A, Li J, Niyato D, Poor HV (2021) Federated learning meets blockchain in edge computing: Opportunities and challenges. IEEE Internet of Things Journal 8(16):12806–12825

    Article  Google Scholar 

  27. Schultze JL, Büttner M, Becker M (2022) Swarm immunology: harnessing blockchain technology and artificial intelligence in human immunology. Nature Reviews Immunology 22(7):401–403

    Article  Google Scholar 

  28. Gerussi A, Scaravaglio M, Cristoferi L, Verda D, Milani C, De Bernardi E, Ippolito D, Asselta R, Invernizzi P, Kather JN et al (2022) Artificial intelligence for precision medicine in autoimmune liver disease. Frontiers in Immunology 13

  29. Wu J, Dong Q, Zhang J, Su Y, Wu T, Caselli RJ, Reiman EM, Ye J, Lepore N, Chen K et al (2021) Federated morphometry feature selection for hippocampal morphometry associated beta-amyloid and tau pathology. Frontiers in Neuroscience, 1585

  30. Han J, Ma Y, Han Y, Zhang Y, Huang G (2022) Demystifying swarm learning: A new paradigm of blockchain-based decentralized federated learning. arXiv:2201.05286

  31. Garay J, Kiayias A, Leonardos N (2015) The bitcoin backbone protocol: Analysis and applications. In: Advances in Cryptology-EUROCRYPT 2015: 34th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Sofia, Bulgaria, April 26-30, 2015, Proceedings, Part II, pp. 281–310. Springer

  32. Sait U, Gokul Lal K, Prajapati S, Bhaumik R, Kumar T, Sanjana S, Bhalla K (2020) Curated dataset for COVID-19 posterior-anterior chest radiography images (X-Rays). Mendeley Data, V1

  33. Reiss A, Stricker D (2012) Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp.108–109. IEEE

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62201219, China Postdoctoral Science Foundation 2021TQ0028, Beijing Natural Science Foundation L211013, and the State Key Laboratory of Rail Traffic Control and Safety (Contract No. RCS2023K010), Beijing Jiaotong University.

Author information

Authors and Affiliations

  1. Research Center of 6G Mobile Communications, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Guoqiang Zhang, Yueyue Dai & Jian Wu

  2. College of Engineering, Abu Dhabi University, Abu Dhabi, UAE

    Xiaojie Zhu

  3. The State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Yunlong Lu

Authors
  1. Guoqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yueyue Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojie Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yunlong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yueyue Dai.

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

Zhang, G., Dai, Y., Wu, J. et al. Swarm Learning-based Secure and Fair Model Sharing for Metaverse Healthcare. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02236-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-023-02236-1

Keywords

Search

Navigation