<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning on Abhiraj Bibhar</title><link>https://abhiraj.pages.dev/tags/machine-learning/</link><description>Recent content in Machine Learning on Abhiraj Bibhar</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 01 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://abhiraj.pages.dev/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Attention-Guided Federated Learning for Privacy-Preserving Clinical NLP</title><link>https://abhiraj.pages.dev/publications/attention-guided-federated-learning-clinical-nlp/</link><pubDate>Mon, 01 Jun 2026 00:00:00 +0000</pubDate><guid>https://abhiraj.pages.dev/publications/attention-guided-federated-learning-clinical-nlp/</guid><description>&lt;h2 id="abstract"&gt;Abstract&lt;/h2&gt;&#10;&lt;p&gt;Training clinical natural language processing models typically requires centralizing patient records, which is often legally or ethically infeasible across institutions. We introduce an attention-guided federated learning protocol that allows clinical language models to be trained collaboratively across multiple hospital systems without any patient data leaving its host institution.&lt;/p&gt;</description></item></channel></rss>