Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Federated Learning in Healthcare with Unsupervised and Semi-Supervised Methods

  • Conference paper
  • First Online:
Flexible Query Answering Systems (FQAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14113))

Included in the following conference series:

  • 424 Accesses

Abstract

The federated paradigm has made possible the development of techniques capable of solving advanced problems in the healthcare field through the protection of data privacy. However, most existing research is centered around supervised methods and real world data tends to be unevenly distributed and scarcely labelled. This paper aims to provide an overview of existing unsupervised and semi-supervised methods implemented in a federated healthcare setting in order to identify state of the art methods and detect current challenges and future lines of research.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. McMahan, H.B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-Efficient learning of deep networks from decentralized data (2016). arXiv. 1602.05629

  2. Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 12 (2019)

    Google Scholar 

  3. Pfitzner, B., Steckhan, N., Bert Arnrich, B.: Federated learning in a medical context: a systematic literature review. ACM Trans. Internet Technol. 21(2), 31 (2021)

    Google Scholar 

  4. Jin, Y., Wei, X., Liu, Y.: Qiang Yang. Towards utilizing unlabeled data in federated learning, A survey and prospective (2020)

    Google Scholar 

  5. Prayitno.: A systematic review of federated learning in the healthcare area: from the perspective of data properties and applications. Appl. Sci. 11, 11191 (2021). https://doi.org/10.3390/app112311191

  6. Fan, C., Hue, J., Huang, J.: Private semi-supervised federated learning. In: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI-22) (2022)

    Google Scholar 

  7. Zhang, L., Shen, B., Barnawi, A., Xi, S., Kumar, N., Wu, Y.: FedDPGAN: Federated differentially private generative adversarial networks framework for the detection of COVID-19 pneumonia. Inf. Syst, Front (2021)

    Google Scholar 

  8. Wu, Q., Chen, X., Zhou, Z., Zhang, J.: FedHome: cloud-edge based personalized federated learning for in-home health monitoring. IEEE Trans. Mobile Comput. 21, 2818–2832 (2020)

    Google Scholar 

  9. Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H.R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al.: Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy. Japan. Med. Image Anal. 70, 101992 (2021)

    Article  Google Scholar 

  10. Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with non-IID data (2018), arXiv:1806.00582

  11. McMahan, H.B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the Artificial Intelligence and Statistics Conference, Fort Lauderdale, FL, USA, pp. 1273–1282 (2017)

    Google Scholar 

  12. Huang, L., Shea, A.L., Qian, H., Masurkar, A., Deng, H., Liu, D.: Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J. Biomed. Inform. 99, 103291 (2019)

    Article  Google Scholar 

  13. Chen, Y., Qin, X., Wang, J., Yu, C., Gao, W.: FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intell. Syst. 35, 83–93 (2020)

    Article  Google Scholar 

  14. Dennis, D.K., Li, T., Smith, V.: Heterogeneity for the Win: one-shot federated clustering. In: Proceedings of the 38th International Conference on Machine Learning, PMLR 139 (2021)

    Google Scholar 

  15. Huang, L., Yin, Y., Fu, Z., Zhang, S., Deng, H., Liu, D.: LoAdaBoost: Loss-based AdaBoost federated machine learning with reduced computational complexity on IID and non-IID intensive care data. PLoS ONE 15, e0230706 (2020)

    Article  Google Scholar 

  16. Tran, K.; Bøtker, J.P., Aframian, A., Memarzadeh, K.: Artificial intelligence for medical imaging. In: Artificial Intelligence in Healthcare; Elsevier: Amsterdam, The Netherlands, pp. 143–162 (2020)

    Google Scholar 

  17. Cohen, I., Cozman, F.G., Sebe, N., Cirelo, M.C., Huang, T.S.: Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction. IEEE Trans. Pattern Anal. Mach. Intell. 26(12), 1553–1566 (2004)

    Article  Google Scholar 

  18. Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M.: Semisupervised learning with deep generative models. Adv. Neural Inf. Process. Syst. 27, 3581–3589 (2014)

    Google Scholar 

  19. Miyato, T., Maeda, S., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979–1993 (2018)

    Article  Google Scholar 

  20. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weightaveraged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30, 1195–1204 (2017)

    Google Scholar 

  21. Berthelot, D., et al.: A holistic approach to semi-supervised learning. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d Alche-Buc, E. Fox, and R. Garnett, editors, Adv. Neural Inf. Process. Syst.32, 5049–5059. Curran Associates Inc (2019)

    Google Scholar 

  22. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  23. Chen, D.-D.,Wang, W., Gao, W., Zhi-Hua Zhou, Z.-H.: Tri-net for semi-supervised deep learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence, pp. 2014–2020. AAAI Press (2018)

    Google Scholar 

  24. Solatidehkordi, Z., Zualkernan, I.: Survey on Recent Trends in Medical Image Classification Using Semi-Supervised Learning. Appl. Sci. 12, 12094 (2022). https://doi.org/10.3390/app122312094

    Article  Google Scholar 

  25. Liang, X., Lin, Y., Fu, H., Zhu, L., Li, X.: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10154–10163 (2022)

    Google Scholar 

  26. Yan, Z., Guoliang, L., Jianhua, F.: A survey on entity alignment of knowledge base. J. Comput. Res. Dev. 53(1), 165–192 (2016). https://doi.org/10.7544/issn1000-1239.2016.20150661

    Article  Google Scholar 

  27. Roy, A.G., Siddiqui, S., Pölsterl, S., Navab, N., Wachinger, C.: BrainTorrent: a Peer-to-Peer Environment for Decentralized Federated Learning (2019). https://doi.org/10.48550/arXiv.1905.06731

  28. Singh, S., Rathore, S., Alfarraj, O., Tolba, A., Yoon, B.: A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Futur. Gener. Comput. Syst. 129, 380–388 (2022)

    Article  Google Scholar 

  29. Rizk, E., Sayed, A.H.: A graph federated architecture with privacy preserving learning. In: 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

    Google Scholar 

  30. Lloyd, S.: Least squares quantization in PCM. IEEE Transactions on Information Theory (1982)

    Google Scholar 

  31. Dhillon, I.S., Modha, D.S.: A data-clustering algorithm on distributed memory multiprocessors. In: Large-Scale Parallel Data Mining (2002)

    Google Scholar 

  32. Tasoulis, D.K. Vrahatis, M.N.: Unsupervised distributed clustering. In: Parallel and Distributed Computing and Networks (2004)

    Google Scholar 

  33. Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al.: A densitybased algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery & Data Mining (1996)

    Google Scholar 

  34. Kargupta, H., Huang, W., Sivakumar, K., Johnson, E.: Distributed clustering using collective principal component analysis. Knowl. Inf. Syst. 32, 422–448 (2001) https://doi.org/10.1007/PL00011677

  35. Feldman, D., Sugaya, A., Rus, D.: An effective coreset compression algorithm for large scale sensor networks. In: International Conference on Information Processing in Sensor Networks (2012)

    Google Scholar 

  36. Bachem, O., Lucic, M., Krause, A.: Scalable k-means clustering via lightweight coresets. In: International Conference on Knowledge Discovery & Data Mining (2018)

    Google Scholar 

  37. Stallmann, M., Wilbik, A.: Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM) (2022). arXiv:2201.07316v1

  38. Ghosh, A., Chung, J., Yin, D., Ramchandran, K.: An Efficient Framework for Clustered Federated Learning. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 19586–19597. Curran Associates Inc (2020)

    Google Scholar 

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

    Google Scholar 

  40. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), Article 15 (2009). https://doi.org/10.1145/1541880.1541882

  41. Ukil, A., Bandyoapdhyay, S., Puri, C., A. Pal, A.: IoT Healthcare Analytics: The Importance of Anomaly Detection. In: 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), Crans-Montana, Switzerland, pp. 994–997 (2016), https://doi.org/10.1109/AINA.2016.158

  42. Siniosoglou, I., et al.: Federated intrusion detection in NG-IoT healthcare systems: an adversarial approach. In: ICC 2021 - IEEE International Conference on Communications (2021)

    Google Scholar 

  43. Liu, F.T., Ting, K.M., Zhou, Z.-H.: Isolation forest. In: 2008 8th IEEE International Conference on Data Mining, Pisa, Italy. IEEE (2008)

    Google Scholar 

  44. Cavallin, F., Mayer, R.: Anomaly Detection from Distributed Data Sources via Federated Learning. In: Barolli, L., Hussain, F., Enokido, T. (eds.) AINA 2022. LNNS, vol. 450, pp. 317–328. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99587-4_27

    Chapter  Google Scholar 

  45. Bercea, C.I., Wiestler, B., Rueckert, D., et al.: Federated disentangled representation learning for unsupervised brain anomaly detection. Nat. Mach. Intell. 4, 685–695 (2022). https://doi.org/10.1038/s42256-022-00515-2

    Article  Google Scholar 

  46. Baur, C., Denner, S., Wiestler, B., Navab, N., Albarqouni, S.: Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study. Med. Image Anal. 69, 101952 (2021)

    Google Scholar 

  47. Grammenos, A., Mendoza Smith, R., Crowcroft, J., Mascolo, C.: Federated principal component analysis. Adv. Neural. Inf. Process. Syst. 33, 6453–6464 (2020)

    Google Scholar 

  48. Cui, W., Zhao, Y., Xu, J., Cheng, H.: Federated sufficient dimension reduction through high-dimensional sparse sliced inverse regression (2023). arXiv preprint arXiv:2301.09500

  49. Chai, D., Wang, L., Fu, L., Zhang, J., Chen, K., Yang, Q.: . Federated singular vector decomposition. arXiv e-prints, https://arxiv.org/abs/2105.08925 (2021)

  50. Islam, T. U., Ghasemi, R., Mohammed, N.: Privacy-preserving federated learning model for healthcare data. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0281–0287). IEEE (2022)

    Google Scholar 

  51. Wei, K., et al.: Vertical federated learning: Challenges, methodologies and experiments. arXiv preprint arXiv:2202.04309 (2022)

  52. Yoo, J. H., et .: Personalized federated learning with clustering: non-IID heart rate variability data application. In: 2021 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1046–1051. IEEE (2021)

    Google Scholar 

  53. Molina, C., Prados-Suarez, B., Martinez-Sanchez, B.: Federated Mining of Interesting Association Rules Over EHRs. In: Applying the FAIR Principles to Accelerate Health Research in Europe in the Post COVID-19 Era, pp. 3–7. IOS Press (2021)

    Google Scholar 

  54. Sun, L., Wu, J.: A scalable and transferable federated learning system for classifying healthcare sensor data. IEEE J. Biomed. Health Inf. 27, 866–877 (2022)

    Google Scholar 

  55. Domadiya, N., Rao, U.P.: Privacy preserving distributed association rule mining approach on vertically partitioned healthcare data. Procedia Comput. Sci. 148, 303–312 (2019)

    Article  Google Scholar 

  56. Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans. Knowl. Data Eng. 16(9), 1026–1037 (2004)

    Article  Google Scholar 

  57. Tassa, T.: Secure mining of association rules in horizontally distributed databases. IEEE Trans. Knowl. Data Eng. 26(4), 970–983 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge support for this work from the Grant PID2021-123960OB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe.

Author information

Authors and Affiliations

  1. Department of Computer Science and A.I., Research Centre for Information and Communication Technologies (CITIC -UGR), University of Granada, Granada, Spain

    Juan Paños-Basterra, M. Dolores Ruiz & Maria J. Martin-Bautista

Authors
  1. Juan Paños-Basterra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Dolores Ruiz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria J. Martin-Bautista
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Paños-Basterra .

Editor information

Editors and Affiliations

  1. Legind Technologies, Esbjerg V, Denmark

    Henrik Legind Larsen

  2. University of Granada, Granada, Spain

    Maria J. Martin-Bautista

  3. University of Granada, Granada, Spain

    M. Dolores Ruiz

  4. Roskilde University, Roskilde, Denmark

    Troels Andreasen

  5. Consiglio Nazionale delle Ricerche, Milano, Italy

    Gloria Bordogna

  6. Ghent University, Gent, Belgium

    Guy De Tré

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paños-Basterra, J., Ruiz, M.D., Martin-Bautista, M.J. (2023). Federated Learning in Healthcare with Unsupervised and Semi-Supervised Methods. In: Larsen, H.L., Martin-Bautista, M.J., Ruiz, M.D., Andreasen, T., Bordogna, G., De Tré, G. (eds) Flexible Query Answering Systems. FQAS 2023. Lecture Notes in Computer Science(), vol 14113. Springer, Cham. https://doi.org/10.1007/978-3-031-42935-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-42935-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-42934-7

  • Online ISBN: 978-3-031-42935-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation