<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Aggregation | Zhonghao Chen</title><link>https://diogeneschen.github.io/tags/aggregation/</link><atom:link href="https://diogeneschen.github.io/tags/aggregation/index.xml" rel="self" type="application/rss+xml"/><description>Aggregation</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Thu, 01 Jan 2026 00:00:00 +0000</lastBuildDate><image><url>https://diogeneschen.github.io/media/icon_hu18301096222111465208.png</url><title>Aggregation</title><link>https://diogeneschen.github.io/tags/aggregation/</link></image><item><title>FedMECA: Scalable Federated Learning via Memory-Efficient and Concurrent Aggregation</title><link>https://diogeneschen.github.io/publication/fedmeca-cais-2026/</link><pubDate>Thu, 01 Jan 2026 00:00:00 +0000</pubDate><guid>https://diogeneschen.github.io/publication/fedmeca-cais-2026/</guid><description>&lt;p>FedMECA improves federated learning scalability by making aggregation more memory-efficient and concurrent, targeting complex FL workflows with large model and client counts.&lt;/p></description></item></channel></rss>