---
type: publication
title: 'Learn What Matters: Data Pruning for Efficient Decentralized Learning'
description: Manuscript on data pruning for efficient decentralized learning.
tags:
  - en
  - publication
  - paper
  - under-review
  - private
timestamp: '2026-05-06T06:22:22+08:00'
modified: '2026-08-09T18:32:45.772Z'
content_hash: 'sha256:68f5b36b9bb3fb0a105dfd49aa760575f59d0b5629c6389306f6abe8f7612e98'
reviewed_at: '2026-08-10T02:34:00+08:00'
review_due: '2026-09-08'
name: 'Learn What Matters: Data Pruning for Efficient Decentralized Learning'
summary: Manuscript on data pruning for efficient decentralized learning.
hidden: true
authors:
  - Xinbao Qiao
  - Xunhao Jiang
  - Zuozhu Liu
  - Peng Sun
  - Meng Zhang
venue: under review
year: 2025
status: under review
publication_type: Journal manuscript
---
This hidden record preserves bibliographic metadata for a manuscript by **Xinbao Qiao** and collaborators. It is not part of the public wiki index, and this page does not assert a current public version or review outcome.

## Status

The record is limited to bibliographic metadata retained from an earlier internal snapshot. No paper package, poster, abstract, or public preprint was available in the local project materials reviewed on 2026-08-10.

## Placement

The manuscript belongs to [[AI_and_Networks]], [[Distributed_Learning]], [[Data_Centric_Machine_Learning|Data Centric ML]], and [[Data_Selection]]. Its title indicates a focus on identifying useful data for decentralized learning, with the goal of improving efficiency under distributed-data and communication constraints.

## Update workflow

When the manuscript becomes public or a source package is provided, this page should be expanded with the same structure used by the accepted paper pages: overview, method, key takeaways, results, and placement. The takeaways should emphasize high-level lessons about decentralized learning and data value rather than formula-level details.
