{"id":15091,"date":"2024-01-22T09:08:06","date_gmt":"2024-01-22T08:08:06","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=15091"},"modified":"2024-06-07T12:40:05","modified_gmt":"2024-06-07T10:40:05","slug":"application-of-federated-learning-to-medical-data-at-large-scale","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/application-of-federated-learning-to-medical-data-at-large-scale\/","title":{"rendered":"Application of Federated Learning to Medical Data at Large Scale"},"content":{"rendered":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Federated learning is a technique which allows training machine learning models in a distributed way without transferring the data from its source. It has thus potential applications in analysis of medical data, where privacy and security issues are of great importance. Although there are examples of using federated approaches to analysis of medical data, there is still need for research in this area and for experiments in real distributed environments.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">This thesis aims to explore the application of federated learning techniques to solve specific problems in medical data analysis, such as MRI translation and estimation of brain microstructural parameters. The project will use publicly available datasets with varying acquisition parameters and resolution. The research will be carried out using a specific library for federated learning called Flower and HPC resources from PL-Grid.<\/p>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">The main objective of the research is to analyze the different federated learning techniques and determine their potential and limitations when applied to medical imaging. Additionally, the research aims to identify the necessary steps to make these methods more accessible and practical for real-world applications.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Federated learning is a technique which allows training machine learning models in a distributed way without transferring the data from its source. It has thus potential applications in analysis of medical data, where privacy and security issues are of great importance. Although there are examples of using federated approaches to analysis of medical data, there is still need for research in this area and for experiments in real distributed environments.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[7],"research_team":[16],"class_list":["post-15091","research","type-research","status-publish","hentry","research_type-research-topics","research_team-extreme-scale-data-and-computing"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Application of Federated Learning to Medical Data at Large Scale - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/application-of-federated-learning-to-medical-data-at-large-scale\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Application of Federated Learning to Medical Data at Large Scale\" \/>\n<meta property=\"og:description\" content=\"Federated learning is a technique which allows training machine learning models in a distributed way without transferring the data from its source. It has thus potential applications in analysis of medical data, where privacy and security issues are of great importance. Although there are examples of using federated approaches to analysis of medical data, there is still need for research in this area and for experiments in real distributed environments.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/application-of-federated-learning-to-medical-data-at-large-scale\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2024-06-07T10:40:05+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/application-of-federated-learning-to-medical-data-at-large-scale\\\/\",\"name\":\"Application of Federated Learning to Medical Data at Large Scale - 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The research will be carried out using a specific library for federated learning called Flower and HPC resources from PL-Grid.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"20px","epAnimationGeneratedClass":"edplus_anim-yQ8fHX","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-mmKlNc","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The main objective of the research is to analyze the different federated learning techniques and determine their potential and limitations when applied to medical imaging. 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