---
format: "aidr-story-markdown/v1"
id: "c2516e172926e8e6bc5453d8098a8c5b15a5c3209f95a30726004afcd3f76f88"
canonical_url: "https://aidr.today/c2516e17?lang=en"
title: "An Asynchronous Federated Learning Platform for Lung Adenocarcinoma Grading via Foundation Models and Structural Pruning"
lang: "en"
requested_lang: "en"
available_langs: ["en","vi"]
translation_fallback: null
fallback_fields: []
published_at: "2026-11-13T05:00:00.000Z"
category: "Research"
topics: ["research"]
source_urls: ["https://arxiv.org/abs/2611.14590"]
summary: "A web-based federated learning system was proposed to classify lung adenocarcinoma into poorly differentiated (PD) and well-to-moderately differentiated (WMD) subtypes. The system shares only locally trained model weights without sharing patient data between institutions. Joint training with CPTAC and TCGA outperformed single-institution training on external validation."
---

# An Asynchronous Federated Learning Platform for Lung Adenocarcinoma Grading via Foundation Models and Structural Pruning

> [Open the canonical story](<https://aidr.today/c2516e17?lang=en>)

**Published:** 2026-11-13T05:00:00.000Z
**Category:** Research
**Topics:** research

## Summary

A web\-based federated learning system was proposed to classify lung adenocarcinoma into poorly differentiated \(PD\) and well\-to\-moderately differentiated \(WMD\) subtypes\. The system shares only locally trained model weights without sharing patient data between institutions\. Joint training with CPTAC and TCGA outperformed single\-institution training on external validation\.

## Sources

- [Story source](<https://arxiv.org/abs/2611.14590>)