{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:59:37Z","timestamp":1776445177573,"version":"3.51.2"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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.<\/jats:p>","DOI":"10.1038\/s41746-020-00323-1","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T10:03:01Z","timestamp":1600077781000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2346,"title":["The future of digital health with federated learning"],"prefix":"10.1038","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0241-9334","authenticated-orcid":false,"given":"Nicola","family":"Rieke","sequence":"first","affiliation":[]},{"given":"Jonny","family":"Hancox","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1081-2830","authenticated-orcid":false,"given":"Wenqi","family":"Li","sequence":"additional","affiliation":[]},{"given":"Fausto","family":"Milletar\u00ec","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3662-8743","authenticated-orcid":false,"given":"Holger R.","family":"Roth","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-2211","authenticated-orcid":false,"given":"Shadi","family":"Albarqouni","sequence":"additional","affiliation":[]},{"given":"Spyridon","family":"Bakas","sequence":"additional","affiliation":[]},{"given":"Mathieu N.","family":"Galtier","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5733-2127","authenticated-orcid":false,"given":"Bennett A.","family":"Landman","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6626-2463","authenticated-orcid":false,"given":"Klaus","family":"Maier-Hein","sequence":"additional","affiliation":[]},{"given":"S\u00e9bastien","family":"Ourselin","sequence":"additional","affiliation":[]},{"given":"Micah","family":"Sheller","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8081-7376","authenticated-orcid":false,"given":"Ronald M.","family":"Summers","sequence":"additional","affiliation":[]},{"given":"Andrew","family":"Trask","sequence":"additional","affiliation":[]},{"given":"Daguang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Maximilian","family":"Baust","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1284-2558","authenticated-orcid":false,"given":"M. Jorge","family":"Cardoso","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"key":"323_CR1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436 (2015).","journal-title":"Nature"},{"key":"323_CR2","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1001\/jamainternmed.2018.7117","volume":"179","author":"F Wang","year":"2019","unstructured":"Wang, F., Casalino, L. P. & Khullar, D. Deep learning in medicine\u2014promise, progress, and challenges. JAMA Intern. Med. 179, 293\u2013294 (2019).","journal-title":"JAMA Intern. Med."},{"key":"323_CR3","doi-asserted-by":"publisher","first-page":"2113","DOI":"10.1148\/rg.2017170077","volume":"37","author":"G Chartrand","year":"2017","unstructured":"Chartrand, G. et al. Deep learning: a primer for radiologists. Radiographics 37, 2113\u20132131 (2017).","journal-title":"Radiographics"},{"key":"323_CR4","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","volume":"24","author":"J De Fauw","year":"2018","unstructured":"De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342 (2018).","journal-title":"Nat. Med."},{"key":"323_CR5","doi-asserted-by":"crossref","unstructured":"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\u2013852 (IEEE, 2017).","DOI":"10.1109\/ICCV.2017.97"},{"key":"323_CR6","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2458-14-1144","volume":"14","author":"WG Van Panhuis","year":"2014","unstructured":"Van Panhuis, W. G. et al. A systematic review of barriers to data sharing in public health. BMC Public Health 14, 1144 (2014).","journal-title":"BMC Public Health"},{"key":"323_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41467-019-10933-3","volume":"10","author":"L Rocher","year":"2019","unstructured":"Rocher, L., Hendrickx, J. M. & De Montjoye, Y.-A. Estimating the success of re-identifications in incomplete datasets using generative models. Nat. Commun. 10, 1\u20139 (2019).","journal-title":"Nat. Commun."},{"key":"323_CR8","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1056\/NEJMc1908881","volume":"381","author":"CG Schwarz","year":"2019","unstructured":"Schwarz, C. G. et al. Identification of anonymous mri research participants with face-recognition software. N. Engl. J. Med. 381, 1684\u20131686 (2019).","journal-title":"N. Engl. J. Med."},{"key":"323_CR9","unstructured":"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\u20131282. https:\/\/scholar.google.de\/scholar?hl=de&as_sdt=0%2C5&q=Communicationefficient+learning+of+deep+networks+from+decentralized+data&btnG= (2017)."},{"key":"323_CR10","doi-asserted-by":"crossref","unstructured":"Li, T., Sahu, A. K., Talwalkar, A. & Smith, V. Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine 37, 50\u201360 (IEEE, 2020).","DOI":"10.1109\/MSP.2020.2975749"},{"key":"323_CR11","first-page":"12","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang, Q., Liu, Y., Chen, T. & Tong, Y. Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10, 12 (2019).","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"323_CR12","unstructured":"Kairouz, P. et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)."},{"key":"323_CR13","doi-asserted-by":"publisher","DOI":"10.2196\/medinform.7744","volume":"6","author":"J Lee","year":"2018","unstructured":"Lee, J. et al. Privacy-preserving patient similarity learning in a federated environment: development and analysis. JMIR Med. Inform. 6, e20 (2018).","journal-title":"JMIR Med. Inform."},{"key":"323_CR14","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.ijmedinf.2018.01.007","volume":"112","author":"TS Brisimi","year":"2018","unstructured":"Brisimi, T. S. et al. Federated learning of predictive models from federated electronic health records. Int. J. Med. Inform. 112, 59\u201367 (2018).","journal-title":"Int. J. Med. Inform."},{"key":"323_CR15","unstructured":"Roy, A. G., Siddiqui, S., P\u00f6lsterl, S., Navab, N. & Wachinger, C. Braintorrent: a peer-to-peer environment for decentralized federated learning. arXiv preprint arXiv:1905.06731 (2019)."},{"key":"323_CR16","doi-asserted-by":"crossref","unstructured":"Li, W. et al. Privacy-preserving federated brain tumour segmentation. In International Workshop on Machine Learning in Medical Imaging, 133\u2013141 (Springer, 2019).","DOI":"10.1007\/978-3-030-32692-0_16"},{"key":"323_CR17","doi-asserted-by":"crossref","unstructured":"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\u2013104 (Springer, 2018).","DOI":"10.1007\/978-3-030-11723-8_9"},{"key":"323_CR18","doi-asserted-by":"crossref","unstructured":"Li, X. et al. Multi-site fmri analysis using privacy-preserving federated learning and domain adaptation: abide results. arXiv preprint arXiv:2001.05647 (2020).","DOI":"10.1016\/j.media.2020.101765"},{"key":"323_CR19","doi-asserted-by":"publisher","first-page":"103291","DOI":"10.1016\/j.jbi.2019.103291","volume":"99","author":"L Huang","year":"2019","unstructured":"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. 99, 103291 (2019).","journal-title":"J. Biomed. Inform."},{"key":"323_CR20","unstructured":"Xu, J. & Wang, F. Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 (2019)."},{"key":"323_CR21","unstructured":"Roy, A. & Banerjee, A. Ibm\u2019s 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)."},{"key":"323_CR22","unstructured":"Nhs scotland\u2019s 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)."},{"key":"323_CR23","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1055\/s-0039-1677917","volume":"28","author":"M Cuggia","year":"2019","unstructured":"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. 28, 195\u2013202 (2019).","journal-title":"Yearbook Med. Informat."},{"key":"323_CR24","unstructured":"Health Data Research UK. https:\/\/www.hdruk.ac.uk\/ (Health Data Research UK, 2020) (Accessed 10 Feb 2020)."},{"key":"323_CR25","doi-asserted-by":"publisher","unstructured":"Sporns, O., Tononi, G. & K\u00f6tter, R. The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1, e42, https:\/\/doi.org\/10.1371\/journal.pcbi.0010042 (2005).","DOI":"10.1371\/journal.pcbi.0010042"},{"key":"323_CR26","doi-asserted-by":"publisher","unstructured":"Sudlow, C. et al. Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779. https:\/\/doi.org\/10.1371\/journal.pmed.1001779 (2015).","DOI":"10.1371\/journal.pmed.1001779"},{"key":"323_CR27","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark, K. et al. The cancer imaging archive (tcia): maintaining and operating a public information repository. J. Digit. Imaging. 26, 1045\u20131057 (2013).","journal-title":"J. Digit. Imaging."},{"key":"323_CR28","doi-asserted-by":"crossref","unstructured":"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\u20132106 (IEEE, 2017).","DOI":"10.1109\/CVPR.2017.369"},{"key":"323_CR29","doi-asserted-by":"publisher","first-page":"036501","DOI":"10.1117\/1.JMI.5.3.036501","volume":"5","author":"K Yan","year":"2018","unstructured":"Yan, K., Wang, X., Lu, L. & Summers, R. M. Deeplesion: automated mining of large-scale lesion annotations and universal lesion detection with deep learning. J Med. Imaging. 5, 036501 (2018).","journal-title":"J Med. Imaging."},{"key":"323_CR30","first-page":"A68","volume":"19","author":"K Tomczak","year":"2015","unstructured":"Tomczak, K., Czerwi\u0144ska, P. & Wiznerowicz, M. The cancer genome atlas (tcga): an immeasurable source of knowledge. Contemp. Oncol. 19, A68 (2015).","journal-title":"Contemp. Oncol."},{"key":"323_CR31","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack Jr.","year":"2008","unstructured":"Jack Jr., C. R. et al. The alzheimer\u2019s disease neuroimaging initiative (adni): Mri methods. J. Magn. Reson. Imaging 27, 685\u2013691 (2008).","journal-title":"J. Magn. Reson. Imaging"},{"key":"323_CR32","unstructured":"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)."},{"key":"323_CR33","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giy065","volume":"7","author":"G Litjens","year":"2018","unstructured":"Litjens, G. et al. 1399 h&e-stained sentinel lymph node sections of breast cancer patients: the camelyon dataset. GigaScience 7, giy065 (2018).","journal-title":"GigaScience"},{"key":"323_CR34","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34, 1993\u20132024 (2014).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"323_CR35","unstructured":"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)."},{"key":"323_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas, S. et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017).","journal-title":"Sci. Data"},{"key":"323_CR37","unstructured":"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)."},{"key":"323_CR38","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005203","volume":"12","author":"F-C Yeh","year":"2016","unstructured":"Yeh, F.-C. et al. Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints. PLoS Comput. Biol. 12, e1005203 (2016).","journal-title":"PLoS Comput. Biol."},{"key":"323_CR39","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1093\/jamia\/ocy017","volume":"25","author":"K Chang","year":"2018","unstructured":"Chang, K. et al. Distributed deep learning networks among institutions for medical imaging. J. Am. Med. Inform. Assoc. 25, 945\u2013954 (2018).","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"323_CR40","doi-asserted-by":"crossref","unstructured":"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).","DOI":"10.1109\/SP.2017.41"},{"key":"323_CR41","unstructured":"Sablayrolles, A., Douze, M., Ollivier, Y., Schmid, C. & J\u00e9gou, H. White-box vs black-box: Bayes optimal strategies for membership inference. In Chaudhuri, K. & Salakhutdinov, R. (eds) Proceedings of the 36th International Conference on Machine Learning, {ICML} 97, 5558\u20135567. http:\/\/proceedings.mlr.press\/v97\/sablayrolles19a.html (PMLR, 2019)."},{"key":"323_CR42","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Understanding deep learning requires rethinking generalization. In 5th International Conference on Learning Representations, {ICLR}. https:\/\/openreview.net\/forum?id=Sy8gdB9xx, (OpenReview.net, 2017)."},{"key":"323_CR43","unstructured":"Carlini, N., Liu, C., Erlingsson, \u00da., Kos, J. & Song, D. The secret sharer: evaluating and testing unintended memorization in neural networks. In Heninger, N. & Traynor, P. (eds) 28th {USENIX} Security Symposium ({USENIX} Security 19, 267\u2013284. https:\/\/www.usenix.org\/conference\/usenixsecurity19\/presentation\/carlini ({USENIX} Association, Santa Clara, CA, USA, 2019)."},{"key":"323_CR44","doi-asserted-by":"crossref","unstructured":"Abadi, M. et al. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308\u2013318 (ACM, 2016).","DOI":"10.1145\/2976749.2978318"},{"key":"323_CR45","doi-asserted-by":"crossref","unstructured":"Shokri, R. & Shmatikov, V. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, 1310\u20131321 (ACM, 2015).","DOI":"10.1145\/2810103.2813687"},{"key":"323_CR46","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1148\/radiol.2019190613","volume":"291","author":"CP Langlotz","year":"2019","unstructured":"Langlotz, C. P. et al. A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 nih\/rsna\/acr\/the academy workshop. Radiology 291, 781\u2013791 (2019).","journal-title":"Radiology"},{"key":"323_CR47","doi-asserted-by":"publisher","unstructured":"Kim, Y., Sun, J., Yu, H. & Jiang, X. Federated Tensor Factorization for Computational Phenotyping. In Proceedings of the 23rd {ACM} {SIGKDD} International Conference on Knowledge Discoveryand Data Mining. 887\u2013895. https:\/\/doi.org\/10.1145\/3097983.3098118 (ACM, Halifax, NS, Canada, 2017).","DOI":"10.1145\/3097983.3098118"},{"key":"323_CR48","unstructured":"He, C., Annavaram, M. & Avestimehr, S. Fednas: Federated deep learning via neural architecture search. https:\/\/sites.google.com\/view\/cvpr20-nas\/ (2020)."},{"key":"323_CR49","unstructured":"Trustworthy federated data analytics (tfda). https:\/\/tfda.hmsp.center\/ (2020) (Accessed 28 May 2020)."},{"key":"323_CR50","unstructured":"Joint Imaging Platform (Jip). https:\/\/jip.dktk.dkfz.de\/jiphomepage\/ (2020) (Accessed 28 May 2020)."},{"key":"323_CR51","unstructured":"Medical institutions collaborate to improve mammogram assessment ai. https:\/\/blogs.nvidia.com\/blog\/2020\/04\/15\/federated-learning-mammogram-assessment\/ (2020) (Accessed 28 May 2020)."},{"key":"323_CR52","unstructured":"Healthchain consortium. https:\/\/www.substra.ai\/en\/healthchain-project (2020) (Accessed 28 May 2020)."},{"key":"323_CR53","unstructured":"The federated tumor segmentation (fets) initiative. https:\/\/www.fets.ai (2020) (Accessed 28 May 2020)."},{"key":"323_CR54","unstructured":"Machine learning ledger orchestration for drug discovery. https:\/\/cordis.europa.eu\/project\/id\/831472 (2020). Accessed 28 May 2020."},{"key":"323_CR55","unstructured":"Kone\u010dny`, J., McMahan, H. B., Ramage, D. & Richt\u00e1rik, P. Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527 (2016)."},{"key":"323_CR56","unstructured":"Lalitha, A., Kilinc, O. C., Javidi, T. & Koushanfar, F. Peer-to-peer federated learning on graphs. arXiv preprint arXiv:1901.11173 (2019)."},{"key":"323_CR57","unstructured":"Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A. & Smith, V. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)."},{"key":"323_CR58","unstructured":"Zhao, Y. et al. Federated learning with non-iid data. arxivabs\/1806.00582 (2018)."},{"key":"323_CR59","unstructured":"Li, X., Huang, K., Yang, W., Wang, S. & Zhang, Z. On the convergence of fedavg on non-IID data. https:\/\/openreview.net\/forum?id=HJxNAnVtDS (2020)."},{"key":"323_CR60","doi-asserted-by":"crossref","unstructured":"Wu, B. et al. P3sgd: patient privacy preserving SGD for regularizing deep CNNs in pathological image classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2099\u20132108) (2019).","DOI":"10.1109\/CVPR.2019.00220"},{"key":"323_CR61","unstructured":"Zhu, L., Liu, Z. & Han, S. Deep leakage from gradients. In Wallach, H. M. et al. (eds) Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems, 14747\u201314756. http:\/\/papers.nips.cc\/paper\/9617-deep-leakage-from-gradients (2019)."},{"key":"323_CR62","doi-asserted-by":"publisher","unstructured":"Wang, Z. et al. Beyond inferring class representatives: user-level privacy leakage from federated learning. In 2019 {IEEE} Conferenceon Computer Communications, {INFOCOM} 2512\u20132520. https:\/\/doi.org\/10.1109\/INFOCOM.2019.8737416 (IEEE, Paris, France, 2019).","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"323_CR63","doi-asserted-by":"crossref","unstructured":"Hitaj, B., Ateniese, G. & Perez-Cruz, F. Deep models under the gan: information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS\u201917, 603\u2013618 (Association for Computing Machinery, New York, NY, USA, 2017).","DOI":"10.1145\/3133956.3134012"},{"key":"323_CR64","unstructured":"Ghorbani, A. & Zou, J. Data shapley: Equitable valuation of data for machine learning. In International Conference on Machine Learning (pp. 2242-2251) (2019)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00323-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00323-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00323-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T02:34:51Z","timestamp":1670380491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-020-00323-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,14]]},"references-count":64,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["323"],"URL":"https:\/\/doi.org\/10.1038\/s41746-020-00323-1","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,14]]},"assertion":[{"value":"17 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"R.M.S. receives royalties from iCAD, ScanMed, Philips, Translation Holdings and Ping An. His lab has received research support from Ping An and NVIDIA. S.B. is supported by the National Institutes of Health (NIH). M.N.G. is supported by the HealthChain (BPIFrance) and Melloddy (IMI2) projects. A.T. is an employee of Google\u2019s DeepMind. S.O. and M.J.C. are founders and shareholders of Brainminer, llc. The other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"119"}}