{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T00:16:08Z","timestamp":1775952968265,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s11227-020-03509-2","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T11:02:41Z","timestamp":1606302161000},"page":"6197-6213","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Parallelization of the self-organized maps algorithm for federated learning on distributed sources"],"prefix":"10.1007","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7255-5035","authenticated-orcid":false,"given":"Ivan","family":"Kholod","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrey","family":"Rukavitsyn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexey","family":"Paznikov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergei","family":"Gorlatch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"key":"3509_CR1","unstructured":"Dehghani Z (2019) How to move beyond a monolithic data lake to a distributed data mesh. https:\/\/martinfowler.com\/articles\/data-monolith-to-mesh.html"},{"key":"3509_CR2","doi-asserted-by":"crossref","unstructured":"Voigt P, Von dem Bussche A (2017) The EU general data protection regulation (GDPR). In: A practical guide, 1st ed. Springer International Publishing, Cham","DOI":"10.1007\/978-3-319-57959-7_1"},{"key":"3509_CR3","unstructured":"California Consumer Privacy Act Home Page. https:\/\/www.caprivacy.org\/"},{"key":"3509_CR4","unstructured":"Konecn\u00fd J, Brendan McMahan H, Ramage D, Richt\u00e1rik P (2016) Federated optimization: distributed machine learning for on-device intelligence. arXiv:CoRRabs\/1610.02527(2016)"},{"issue":"2","key":"3509_CR5","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1145\/3298981","volume":"10","author":"Q Yang","year":"2019","unstructured":"Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2):12","journal-title":"ACM Trans Intell Syst Technol"},{"key":"3509_CR6","doi-asserted-by":"crossref","unstructured":"Kohonen T (2001) Self-organizing maps (Third Extended Edition), New York","DOI":"10.1007\/978-3-642-56927-2"},{"key":"3509_CR7","doi-asserted-by":"publisher","unstructured":"Kholod I, Shorov A, Efimova M, Gorlatch S (2019) Parallelization of algorithms for mining data from distributed sources. PaCT-2019. Springer. LNCS, pp 289\u2013303 https:\/\/doi.org\/10.1007\/978-3-030-25636-4_23","DOI":"10.1007\/978-3-030-25636-4_23"},{"key":"3509_CR8","doi-asserted-by":"crossref","unstructured":"Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning: data mining, inference, and prediction. Springer","DOI":"10.1007\/978-0-387-21606-5"},{"key":"3509_CR9","unstructured":"Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. In Proceedings of Operating Systems Design and Implementation. San Francisco, CA"},{"key":"3509_CR10","unstructured":"Gorlatch S, Cole M (2011) Parallel Skeletons. In: Padua D (ed.) Encyclopedia of parallel computing. Springer"},{"issue":"2","key":"3509_CR11","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1023\/A:1009817804059","volume":"3","author":"RD Lawrence","year":"1999","unstructured":"Lawrence RD, Almasi GS, Rushmeier HE (1999) A scalable parallel algorithm for selfor-ganizing maps with applications to sparse data mining problems. Data Min Knowl Disc 3(2):171\u2013195","journal-title":"Data Min Knowl Disc"},{"key":"3509_CR12","unstructured":"Fort J, Letr\u00e9my P, Cottrell M (2002) Advantages and drawbacks of the Batch Kohonen algo-rithm. ESANN"},{"key":"3509_CR13","doi-asserted-by":"publisher","unstructured":"Weichel Ch (2010) Adapting self-organizing maps to the mapreduce programming paradigm. STeP, pp 119\u2013131. https:\/\/doi.org\/10.1524\/9783486853162.119","DOI":"10.1524\/9783486853162.119"},{"key":"3509_CR14","doi-asserted-by":"publisher","unstructured":"Sarazin T, Azzag H, Lebbah M (2014) SOM Clustering using spark-mapreduce. In: 2014 IEEE 28th International Parallel & Distributed Processing Symposium Workshops, pp 1727\u20131734 https:\/\/doi.org\/10.1109\/IPDPSW.2014.192","DOI":"10.1109\/IPDPSW.2014.192"},{"key":"3509_CR15","doi-asserted-by":"publisher","unstructured":"Dafonte C, Garabato D, \u00c1lvarez MA, Manteiga M (2018) Distributed fast self-organized maps for massive spectrophotometric data analysis. Sensors (Basel) 18(5):1419. Published 2018 May 3. https:\/\/doi.org\/10.3390\/s18051419","DOI":"10.3390\/s18051419"},{"key":"3509_CR16","unstructured":"Flavius LG, Jose Alfredo FC (2008) Parallel self-organizing maps with application in clustering distributed data. Neural Networks. IJCNN 2008. IEEE International Joint Conference on IEEE World Congress on Computational Intelligence"},{"key":"3509_CR17","unstructured":"Li Q, et al (2020) Federated learning systems: vision, hype and reality for data privacy and protection. arXiv:abs\/1907.09693"},{"key":"3509_CR18","unstructured":"Ingerman A, Ostrowski K (2019) Introducing TensorFlow Federated https:\/\/blog.tensorflow.org\/2019\/03\/introducing-tensorflow-federated.html"},{"key":"3509_CR19","unstructured":"Ryffel Th, Trask A, Dahl M, Wagner B, Mancuso J, Rueckert D, Passerat-Palmbach J (2018) A generic framework for privacy preserving deep learning. preprint arXiv:1811.04017"},{"key":"3509_CR20","unstructured":"An Industrial Grade Federated Learning Framework https:\/\/fate.fedai.org\/"},{"key":"3509_CR21","unstructured":"Paddle Federated Learning https:\/\/github.com\/PaddlePaddle\/PaddleFL"},{"key":"3509_CR22","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.procs.2019.02.068","volume":"150","author":"I Kholod","year":"2019","unstructured":"Kholod I, Kuprianov M, Titkov E, Shorov A, Postnikova E, Mironenko I, Sokolov S (2019) Training normal Bayes classifier on distributed data. Proc Comput Sci 150:389\u2013396. https:\/\/doi.org\/10.1016\/j.procs.2019.02.068","journal-title":"Proc Comput Sci"},{"key":"3509_CR23","doi-asserted-by":"publisher","unstructured":"Kholod I, Rukavitsyn A, Reva N, Shorov A (2019) Distributed data clustering by neural network algorithms. In: Proceedings of the 2019 IEEE Russia Section Young Researchers in Electrical and Electronic Engineering Conference\u2014IEEE. pp 249\u2013253. https:\/\/doi.org\/10.1109\/EIConRus.2019.8657175","DOI":"10.1109\/EIConRus.2019.8657175"},{"key":"3509_CR24","unstructured":"https:\/\/github.com\/Awethon\/SOM-FuncBlock"},{"key":"3509_CR25","unstructured":"https:\/\/github.com\/iiholod\/XelopesFL"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03509-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-020-03509-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-020-03509-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T12:26:14Z","timestamp":1620735974000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-020-03509-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,25]]},"references-count":25,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["3509"],"URL":"https:\/\/doi.org\/10.1007\/s11227-020-03509-2","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,25]]},"assertion":[{"value":"2 November 2020","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2020","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}