<?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 on Abhiraj Bibhar</title><link>https://abhiraj.pages.dev/tags/federated-learning/</link><description>Recent content in Federated Learning on Abhiraj Bibhar</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 01 Dec 2023 00:00:00 +0000</lastBuildDate><atom:link href="https://abhiraj.pages.dev/tags/federated-learning/index.xml" rel="self" type="application/rss+xml"/><item><title>Toward Reproducible Benchmarks in Federated Learning Research</title><link>https://abhiraj.pages.dev/publications/reproducible-benchmarks-federated-learning/</link><pubDate>Fri, 01 Dec 2023 00:00:00 +0000</pubDate><guid>https://abhiraj.pages.dev/publications/reproducible-benchmarks-federated-learning/</guid><description>&lt;h2 id="abstract"&gt;Abstract&lt;/h2&gt;&#10;&lt;p&gt;Federated learning papers frequently report results on incompatible experimental setups — differing client counts, non-IID partitioning strategies, and communication budgets — making cross-paper comparison unreliable. This position paper proposes a minimal standardized benchmark protocol and reports a re-evaluation of six widely cited federated learning methods under matched conditions, finding that reported performance gaps shrink substantially once experimental setup is controlled for.&lt;/p&gt;</description></item></channel></rss>