{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T18:21:58Z","timestamp":1776968518227,"version":"3.51.4"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031438943","type":"print"},{"value":"9783031438950","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-43895-0_33","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"350-360","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["FeSViBS: Federated Split Learning of\u00a0Vision Transformer with\u00a0Block Sampling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7885-6285","authenticated-orcid":false,"given":"Faris","family":"Almalik","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7093-5034","authenticated-orcid":false,"given":"Naif","family":"Alkhunaizi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8802-7107","authenticated-orcid":false,"given":"Ibrahim","family":"Almakky","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6274-9725","authenticated-orcid":false,"given":"Karthik","family":"Nandakumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"33_CR1","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, pp. 308\u2013318 (2016)","DOI":"10.1145\/2976749.2978318"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dib.2020.105474","volume":"30","author":"A Acevedo","year":"2020","unstructured":"Acevedo, A., Merino, A., Alf\u00e9rez, S., Molina, \u00c1., Bold\u00fa, L., Rodellar, J.: A dataset of microscopic peripheral blood cell images for development of automatic recognition systems. Data Brief 30, 1\u20135 (2020)","journal-title":"Data Brief"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Ads, O.S., Alfares, M.M., Salem, M.A.M.: Multi-limb split learning for tumor classification on vertically distributed data. In: 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS), pp. 88\u201392. IEEE (2021)","DOI":"10.1109\/ICICIS52592.2021.9694163"},{"key":"33_CR4","doi-asserted-by":"publisher","unstructured":"Almalik, F., Yaqub, M., Nandakumar, K.: Self-ensembling vision transformer (SEViT) for robust medical image classification. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) Medical Image Computing and Computer Assisted Intervention. MICCAI 2022. LNCS, vol. 13433, pp. 376\u2013386. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16437-8_36","DOI":"10.1007\/978-3-031-16437-8_36"},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Bhojanapalli, S., Chakrabarti, A., Glasner, D., Li, D., Unterthiner, T., Veit, A.: Understanding robustness of transformers for image classification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10231\u201310241 (2021)","DOI":"10.1109\/ICCV48922.2021.01007"},{"key":"33_CR6","doi-asserted-by":"publisher","unstructured":"Codella, N.C.F., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168\u2013172 (2018). https:\/\/doi.org\/10.1109\/ISBI.2018.8363547","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"33_CR7","unstructured":"Combalia, M., et al.: Bcn20000: Dermoscopic lesions in the wild. arXiv:1908.02288 (2019)"},{"issue":"8","key":"33_CR8","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.3390\/diagnostics11081384","volume":"11","author":"Y Dai","year":"2021","unstructured":"Dai, Y., Gao, Y., Liu, F.: TransMed: transformers advance multi-modal medical image classification. Diagnostics 11(8), 1384 (2021)","journal-title":"Diagnostics"},{"issue":"10","key":"33_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","volume":"27","author":"I Dayan","year":"2021","unstructured":"Dayan, I., et al.: Federated learning for predicting clinical outcomes in patients with COVID-19. Nat. Med. 27(10), 1735\u20131743 (2021)","journal-title":"Nat. Med."},{"key":"33_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jnca.2018.05.003","volume":"116","author":"O Gupta","year":"2018","unstructured":"Gupta, O., Raskar, R.: Distributed learning of deep neural network over multiple agents. J. Netw. Comput. Appl. 116, 1\u20138 (2018)","journal-title":"J. Netw. Comput. Appl."},{"issue":"1","key":"33_CR11","doi-asserted-by":"publisher","first-page":"1534","DOI":"10.1038\/s41598-022-05615-y","volume":"12","author":"YJ Ha","year":"2022","unstructured":"Ha, Y.J., Lee, G., Yoo, M., Jung, S., Yoo, S., Kim, J.: Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, x-ray, and cholesterol dataset. Sci. Rep. 12(1), 1534 (2022)","journal-title":"Sci. Rep."},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"33_CR13","first-page":"7232","volume":"34","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Gupta, S., Song, Z., Li, K., Arora, S.: Evaluating gradient inversion attacks and defenses in federated learning. Adv. Neural. Inf. Process. Syst. 34, 7232\u20137241 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1\u20132","key":"33_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"P Kairouz","year":"2021","unstructured":"Kairouz, P., et al.: Advances and open problems in federated learning. Found. Trends \u00ae Mach. Learn. 14(1\u20132), 1\u2013210 (2021)","journal-title":"Found. Trends \u00ae Mach. Learn."},{"key":"33_CR15","unstructured":"Karimireddy, S.P., Kale, S., Mohri, M., Reddi, S., Stich, S., Suresh, A.T.: Scaffold: stochastic controlled averaging for federated learning. In: International Conference on Machine Learning, pp. 5132\u20135143. PMLR (2020)"},{"key":"33_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3505244","volume":"54","author":"S Khan","year":"2021","unstructured":"Khan, S., Naseer, M., Hayat, M., Zamir, S.W., Khan, F.S., Shah, M.: Transformers in vision: a survey. ACM Comput. Surv. (CSUR) 54, 1\u201341 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"33_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Li, Q., He, B., Song, D.: Model-contrastive federated learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10713\u201310722 (2021)","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"33_CR19","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. Proc. Mach. Learn. Syst. 2, 429\u2013450 (2020)","journal-title":"Proc. Mach. Learn. Syst."},{"key":"33_CR20","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR (2017)"},{"key":"33_CR21","unstructured":"Oh, S., et al.: Differentially private cutmix for split learning with vision transformer. In: First Workshop on Interpolation Regularizers and Beyond at NeurIPS 2022 (2022)"},{"key":"33_CR22","unstructured":"Park, S., Kim, G., Kim, J., Kim, B., Ye, J.: Federated split vision transformer for COVID-19 CXR diagnosis using task-agnostic training. In: 35th Conference on Neural Information Processing Systems, NeurIPS 2021, pp. 24617\u201324630 (2021)"},{"key":"33_CR23","doi-asserted-by":"publisher","unstructured":"Poirot, M.G., Vepakomma, P., Chang, K., Kalpathy-Cramer, J., Gupta, R., Raskar, R.: Split learning for collaborative deep learning in healthcare (2019). https:\/\/doi.org\/10.48550\/ARXIV.1912.12115, https:\/\/arxiv.org\/abs\/1912.12115","DOI":"10.48550\/ARXIV.1912.12115"},{"key":"33_CR24","unstructured":"Shamshad, F., et al.: Transformers in medical imaging: a survey. arXiv preprint arXiv:2201.09873 (2022)"},{"key":"33_CR25","unstructured":"du Terrail, J.O., et al.: FLamby: datasets and benchmarks for cross-silo federated learning in realistic healthcare settings. In: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022). https:\/\/openreview.net\/forum?id=GgM5DiAb6A2"},{"issue":"11","key":"33_CR26","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(11), 180161 (2018). https:\/\/doi.org\/10.1038\/sdata.2018.161","journal-title":"Sci. Data"},{"key":"33_CR27","unstructured":"Vepakomma, P., Gupta, O., Swedish, T., Raskar, R.: Split learning for health: Distributed deep learning without sharing raw patient data. arXiv preprint arXiv:1812.00564 (2018)"},{"key":"33_CR28","doi-asserted-by":"publisher","unstructured":"Wightman, R.: Pytorch image models. https:\/\/github.com\/rwightman\/pytorch-image-models (2019). https:\/\/doi.org\/10.5281\/zenodo.4414861","DOI":"10.5281\/zenodo.4414861"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Yang, J., Shi, R., Ni, B.: MedMNIST classification decathlon: a lightweight AutoML benchmark for medical image analysis. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 191\u2013195 (2021)","DOI":"10.1109\/ISBI48211.2021.9434062"},{"key":"33_CR30","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43895-0_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T14:31:28Z","timestamp":1710167488000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43895-0_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031438943","9783031438950"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43895-0_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}