Federated learning has gained popularity for distributed learning without
aggregating sensitive data from clients. But meanwhile, the distributed and
isolated nature of data isolation may be complicated by data quality, making it
more vulnerable to noisy labels. Many efforts exist to defend against the
negative impacts of noisy labels in centralized or federated settings. However,
there is a lack of a benchmark that comprehensively considers the impact of
noisy labels in a wide variety of typical FL settings. In this work, we serve
the first standardized benchmark that can help researchers fully explore
potential federated noisy settings. Also, we conduct comprehensive experiments
to explore the characteristics of these data settings and unravel challenging
scenarios on the federated noisy label learning, which may guide method
development in the future. We highlight the 20 basic settings for more than 5
datasets proposed in our benchmark and standardized simulation pipeline for
federated noisy label learning. We hope this benchmark can facilitate idea
verification in federated learning with noisy labels. \texttt{FedNoisy} is
available at \codeword{this https URL}.

本文提供了一种标准化的基准测试来评估在不同的联邦学习设置下嘈杂标签的影响，为研究人员探索联邦学习中可能出现的数据质量问题提供了帮助。此外还通过对超过 5 个数据集的 20 个基本设置进行的实验，探索了这些数据设置的特征，揭示了联邦嘈杂标签学习中的挑战性情况，为未来的方法开发提供了指导。

FedNoisy：联邦噪声标签学习基准

FedNoisy: Federated Noisy Label Learning Benchmark

Medical image segmentation is an increasingly popular area of research in
medical imaging processing and analysis. However, many researchers who are new
to the field struggle with basic concepts. This tutorial paper aims to provide
an overview of the fundamental concepts of medical imaging, with a focus on
Magnetic Resonance and Computerized Tomography. We will also discuss deep
learning algorithms, tools, and frameworks used for segmentation tasks, and
suggest best practices for method development and image analysis. Our tutorial
includes sample tasks using public data, and accompanying code is available on
GitHub (this https URL). By sharing
our insights gained from years of experience in the field and learning from
relevant literature, we hope to assist researchers in overcoming the initial
challenges they may encounter in this exciting and important area of research.

本教程论文旨在从基础概念、深度学习算法、工具和框架，方法开发和图像分析等方面，为医学影像处理与分析领域的新手提供基础概述，并使用公共数据提供样例任务，并针对方法开发和图像分析提供了最佳实践建议，旨在助力研究者克服初学阶段的挑战。