<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Federated Learning | Joshua Seyi Ibitoye</title><link>https://jsibitoye.com/tags/federated-learning/</link><atom:link href="https://jsibitoye.com/tags/federated-learning/index.xml" rel="self" type="application/rss+xml"/><description>Federated Learning</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 01 Dec 2020 00:00:00 +0000</lastBuildDate><image><url>https://jsibitoye.com/media/icon_hu_982c5d63a71b2961.png</url><title>Federated Learning</title><link>https://jsibitoye.com/tags/federated-learning/</link></image><item><title>AI-Driven Privacy-Preserving Contact Tracing and Pandemic Response Systems</title><link>https://jsibitoye.com/publications/privacy-preserving-pandemic-response/</link><pubDate>Tue, 01 Dec 2020 00:00:00 +0000</pubDate><guid>https://jsibitoye.com/publications/privacy-preserving-pandemic-response/</guid><description>&lt;p&gt;This research introduces a federated learning and zero-trust security model for real-time pandemic response across distributed health networks.&lt;/p&gt;
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