{"version":"1.0","provider_name":"Vinuni Research Website","provider_url":"https:\/\/vinuni.edu.vn\/research","title":"Blockchain and Edge Computing for Federated Learning - Vinuni Research Website","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"Ps3GTc6b9g\"><a href=\"https:\/\/vinuni.edu.vn\/research\/blockchain-and-edge-computing-for-federated-learning\/\">Blockchain and Edge Computing for Federated Learning<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/vinuni.edu.vn\/research\/blockchain-and-edge-computing-for-federated-learning\/embed\/#?secret=Ps3GTc6b9g\" width=\"600\" height=\"338\" title=\"&#8220;Blockchain and Edge Computing for Federated Learning&#8221; &#8212; Vinuni Research Website\" data-secret=\"Ps3GTc6b9g\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(c,l){\"use strict\";var e=!1,o=!1;if(l.querySelector)if(c.addEventListener)e=!0;if(c.wp=c.wp||{},c.wp.receiveEmbedMessage);else if(c.wp.receiveEmbedMessage=function(e){var t=e.data;if(!t);else if(!(t.secret||t.message||t.value));else if(\/[^a-zA-Z0-9]\/.test(t.secret));else{for(var r,s,a,i=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),n=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),o=0;o<n.length;o++)n[o].style.display=\"none\";for(o=0;o<i.length;o++)if(r=i[o],e.source!==r.contentWindow);else{if(r.removeAttribute(\"style\"),\"height\"===t.message){if(1e3<(s=parseInt(t.value,10)))s=1e3;else if(~~s<200)s=200;r.height=s}if(\"link\"===t.message)if(s=l.createElement(\"a\"),a=l.createElement(\"a\"),s.href=r.getAttribute(\"src\"),a.href=t.value,a.host===s.host)if(l.activeElement===r)c.top.location.href=t.value}}},e)c.addEventListener(\"message\",c.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",t,!1),c.addEventListener(\"load\",t,!1);function t(){if(o);else{o=!0;for(var e,t,r,s=-1!==navigator.appVersion.indexOf(\"MSIE 10\"),a=!!navigator.userAgent.match(\/Trident.*rv:11\\.\/),i=l.querySelectorAll(\"iframe.wp-embedded-content\"),n=0;n<i.length;n++){if(!(r=(t=i[n]).getAttribute(\"data-secret\")))r=Math.random().toString(36).substr(2,10),t.src+=\"#?secret=\"+r,t.setAttribute(\"data-secret\",r);if(s||a)(e=t.cloneNode(!0)).removeAttribute(\"security\"),t.parentNode.replaceChild(e,t);t.contentWindow.postMessage({message:\"ready\",secret:r},\"*\")}}}}(window,document);\n<\/script>\n","thumbnail_url":"https:\/\/vinuni.edu.vn\/research\/wp-content\/uploads\/2022\/09\/Prof.-Duc-Tran-1-e1616127735259.jpg","thumbnail_width":612,"thumbnail_height":380,"description":"Abstract Federated Learning (FL) is a recent direction in Machine Learning. Whereas standard learning approaches require centralizing all the training data on one machine, FL enables training with decentralized data privately stored across many machines. This is important because in practice data may be too big to send to the central server or too sensitive [&hellip;]"}