全局准确率（更多通信轮次）
基于COVIDx CXR-3数据集的联邦学习全局准确率实验结果，对比非独立同分布与独立同分布数据下的性能。
基本信息
资源简介
全局准确率（更多通信轮次）
【数据集背景】
本数据集源站标题为《Global accuracy with more communiation rounds.》。源站首次发布：2024-05-15。
【数据内容】
源站原始描述：In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with incre…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 6.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0302539.t006。
【主题与分类】
源站学科分类：Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：notable performance decline、medical imaging applications、increased data heterogeneity、medical imaging、data heterogeneity、xlink ">、study explores、research uncovers、research contributes、recent years。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Global_accuracy_with_more_communiation_rounds_/25830257
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Babar, Muhammad; Qureshi, Basit; Koubaa, Anis (2024). Global accuracy with more communiation rounds.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Global accuracy with more communiation rounds.
- DOI：10.1371/journal.pone.0302539.t006
- 发布日期：2024-05-15
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Global_accuracy_with_more_communiation_rounds_/25830257
命令行下载
curl -L -o "Table 6.xls" "https://ndownloader.figshare.com/files/46352978"
文件清单
Table 6.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
In recent years, Federated Learning (FL) has gained traction as a privacy-centric approach in medical imaging. This study explores the challenges posed by data heterogeneity on FL algorithms, using the COVIDx CXR-3 dataset as a case study. We contrast the performance of the Federated Averaging (FedAvg) algorithm on non-identically and independently distributed (non-IID) data against identically and independently distributed (IID) data. Our findings reveal a notable performance decline with increased data heterogeneity, emphasizing the need for innovative strategies to enhance FL in diverse environments. This research contributes to the practical implementation of FL, extending beyond theoretical concepts and addressing the nuances in medical imaging applications. This research uncovers the inherent challenges in FL due to data diversity. It sets the stage for future advancements in FL strategies to effectively manage data heterogeneity, especially in sensitive fields like healthcare.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0302539.t006（source: https://plos.figshare.com/articles/dataset/Global_accuracy_with_more_communiation_rounds_/25830257）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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