<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/">
  <channel>
    <title>Robust Optimization on Cognaptus</title>
    <link>https://cognaptus.com/tags/robust-optimization/</link>
    <description>Recent content in Robust Optimization on Cognaptus</description>
    <generator>Hugo -- 0.145.0</generator>
    <language>en-us</language>
    <lastBuildDate>Mon, 09 Feb 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://cognaptus.com/tags/robust-optimization/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>When Privacy Meets Chaos: Making Federated Learning Behave</title>
      <link>https://cognaptus.com/blog/2026-02-09-when-privacy-meets-chaos-making-federated-learning-behave/</link>
      <pubDate>Mon, 09 Feb 2026 00:00:00 +0000</pubDate>
      <guid>https://cognaptus.com/blog/2026-02-09-when-privacy-meets-chaos-making-federated-learning-behave/</guid>
      <description>A careful reading of FedCompDP shows why privacy, client heterogeneity, and aggregation stability must be designed together—not bolted together after the model starts shaking.</description>
    </item>
  </channel>
</rss>
