The future of digital health with federated learning

Abstract

Data-driven machine learning (ML) has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by ML primarily because it sits in data silos and privacy concerns restrict access to this data. However, without access to sufficient data, ML will be prevented from reaching its full potential and, ultimately, from making the transition from research to clinical practice. This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.

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References 64

1. 1.
   Deep learning · 2015

   No notice
2. 2.

   Wang F, Casalino LP, Khullar D. Deep learning in medicine—promise, progress, and challenges. JAMA Intern. Med. 2019;179:293-294.

   Not in our library10.1001/jamainternmed.2018.7117

3. 3.

   Chartrand G, et al. Deep learning: a primer for radiologists. Radiographics. 2017;37:2113-2131.

   Not in our library10.1148/rg.2017170077

4. 4.

   De Fauw J, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 2018;24:1342.

   Not in our library10.1038/s41591-018-0107-6

5. 5.

   Sun, C., Shrivastava, A., Singh, S. & Gupta, A. Revisiting unreasonable effectiveness of data in deep learning era. In Proceedings of the IEEE international conference on computer vision, 843–852 (IEEE, 2017).

   Not in our library

6. 6.
   A systematic review of barriers to data sharing in public health · 2014

   No notice
7. 7.
   Estimating the success of re-identifications in incomplete datasets using generative models · 2019

   No notice
8. 8.

   Schwarz CG, et al. Identification of anonymous mri research participants with face-recognition software. N. Engl. J. Med. 2019;381:1684-1686.

   Not in our library10.1056/nejmc1908881

9. 9.

   McMahan, B., Moore, E., Ramage, D., Hampson, S. & y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. In Artificial Intelligence and Statistics, 1273–1282. https://scholar.google.de/scholar?hl=de&as_sdt=0%2C5&q=Communicationefficient+learning+of+deep+networks+from+decentralized+data&btnG= (2017).

   Not in our library

10. 10.

    Li, T., Sahu, A. K., Talwalkar, A. & Smith, V. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine37, 50–60 (IEEE, 2020).

    Not in our library

11. 11.

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

    Not in our library

12. 12.

    Kairouz, P. et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019).

    Not in our library

13. 13.

    Lee J, et al. Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR Med. Inform. 2018;6:e20.

    Not in our library10.2196/medinform.7744

14. 14.

    Brisimi TS, et al. Federated learning of predictive models from federated electronic health records. Int. J. Med. Inform. 2018;112:59-67.

    Not in our library10.1016/j.ijmedinf.2018.01.007

15. 15.

    Roy, A. G., Siddiqui, S., Pölsterl, S., Navab, N. & Wachinger, C. Braintorrent: a peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731 (2019).

    Not in our library

16. 16.

    Li, W. et al. Privacy-preserving federated brain tumour segmentation. In International Workshop on Machine Learning in Medical Imaging, 133–141 (Springer, 2019).

    Not in our library10.1007/978-3-030-32692-0_16

17. 17.

    Sheller, M. J., Reina, G. A., Edwards, B., Martin, J. & Bakas, S. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In International MICCAI Brainlesion Workshop, 92–104 (Springer, 2018).

    Not in our library10.1007/978-3-030-11723-8_9

18. 18.

    Li, X. et al. Multi-site fmri analysis using privacy-preserving federated learning and domain adaptation: abide results. arXiv preprint arXiv:2001.05647 (2020).

    Not in our library10.1016/j.media.2020.101765

19. 19.

    Huang L, et al. Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J. Biomed. Inform. 2019;99:103291.

    Not in our library10.1016/j.jbi.2019.103291

20. 20.

    Xu, J. & Wang, F. Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 (2019).

    Not in our library

21. 21.

    Roy, A. & Banerjee, A. Ibm’s merge healthcare acquisition. https://www.reuters.com/article/us-merge-healthcare-m-a-ibm/ibm-to-buy-merge-healthcare-in-1-billion-deal-idUSKCN0QB1ML20150806 (2015) (Accessed 10 February 2020).

    Not in our library

22. 22.

    Nhs scotland’s national safe haven. https://www.gov.scot/publications/charter-safe-havens-scotland-handling-unconsented-data-national-health-service-patient-records-support-research-statistics/pages/4/ (2015) (Accessed 10 February 2020).

    Not in our library

23. 23.

    Cuggia M, Combes S. The french health data hub and the german medical informatics initiatives: Two national projects to promote data sharing in healthcare. Yearbook Med. Informat. 2019;28:195-202.

    Not in our library10.1055/s-0039-1677917

24. 24.

    Health Data Research UK. https://www.hdruk.ac.uk/ (Health Data Research UK, 2020) (Accessed 10 Feb 2020).

    Not in our library

25. 25.
    The Human Connectome: A Structural Description of the Human Brain · 2005

    No notice
26. 26.
    UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age · 2015

    No notice
27. 27.

    Clark K, et al. The cancer imaging archive (tcia): maintaining and operating a public information repository. J. Digit. Imaging. 2013;26:1045-1057.

    Not in our library10.1007/s10278-013-9622-7

28. 28.

    Wang, X. et al. Chestx-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2097–2106 (IEEE, 2017).

    Not in our library

29. 29.

    Yan K, Wang X, Lu L, Summers RM. Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med. Imaging. 2018;5:036501.

    Not in our library10.1117/1.jmi.5.3.036501

30. 30.

    Tomczak K, Czerwińska P, Wiznerowicz M. The cancer genome atlas (tcga): an immeasurable source of knowledge. Contemp. Oncol. 2015;19:A68.

    Not in our library10.5114/wo.2014.47136

31. 31.

    Jack Jr. CR, et al. The alzheimer’s disease neuroimaging initiative (adni): Mri methods. J. Magn. Reson. Imaging. 2008;27:685-691.

    Not in our library10.1002/jmri.21049

32. 32.

    Grand Challenge-a Platform for End-to-end Development of Machine Learning Solutions in Biomedical Imaging. https://grand-challenge.org/ (2020) (Accessed 24 July 2020).

    Not in our library

33. 33.
    1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset · 2018

    No notice
34. 34.

    Menze BH, et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging. 2014;34:1993-2024.

    Not in our library10.1109/tmi.2014.2377694

35. 35.

    Bakas, S. et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2018).

    Not in our library

36. 36.
    Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features · 2017

    No notice
37. 37.

    Simpson, A. L. et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063 (2019).

    Not in our library

38. 38.
    Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints · 2016

    No notice
39. 39.

    Chang K, et al. Distributed deep learning networks among institutions for medical imaging. J. Am. Med. Inform. Assoc. 2018;25:945-954.

    Not in our library10.1093/jamia/ocy017

40. 40.

    Shokri, R., Stronati, M., Song, C. & Shmatikov, V. Membership inference attacks against machine learning models. In 2017 IEEE Symposium on Security and Privacy (SP), 3-18 (IEEE, 2017).

    Not in our library

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