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. 2022 Apr 25;28(6):1232–1239. doi: 10.1038/s41591-022-01768-5

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a, Histology image analysis workflow for training. b, Histology image analysis workflow for model deployment (inference). c, SL workflow and training cohorts included in this study. On three physically separate bare-metal servers (dashed line), three different sets of clinical data reside. Each server runs an AI process (a program that trains a model on the data) and a network process (a program that handles communication with peers via blockchain). d, Test cohorts included in this study. e, Schematic of the basic SL experiment. For basic SL, the number of epochs is equal for all cohorts, and weights are equal for all cohorts. f, Schematic of the weighted SL experiment. For weighted SL, the number of epochs is larger for small cohorts, and weights are smaller for small cohorts (w_E = weight for the Epi700 cohort, w_D = weight for the DACHS cohort, w_T = weight for the TCGA cohort). Icon credits: a, OpenMoji (CC BY-SA 4.0); c,d, Twitter Twemoji (CC-BY 4.0).