Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model's parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early na\"{i}ve random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.

通常对机器学习分类器进行培训，以最大程度地减少数据集的平均误差。不幸的是，在实践中，这个过程通常会利用训练数据中亚组不平衡引起的虚假相关性，从而导致高平均性能，但跨亚组的性能高度可变。解决此问题的最新工作提出了使用骆驼进行模型修补。这种先前的方法使用生成的对抗网络来执行类内的群间数据增强，需要（a）训练许多计算昂贵的模型以及（b）给定域模型的合成输出的足够质量。在这项工作中，我们提出了RealPatch，这是一个基于统计匹配的简单，更快，更快的数据增强的框架。我们的框架通过使用真实样本增强数据集来执行模型修补程序，从而减轻了为目标任务训练生成模型的需求。我们证明了RealPatch在三个基准数据集，Celeba，Waterbird和IwildCam的一部分中的有效性，显示了最差的亚组性能和二进制分类中亚组性能差距的改进。此外，我们使用IMSITU数据集进行了211个类的实验，在这种设置中，基于生成模型的修补（例如骆驼）是不切实际的。我们表明，RealPatch可以成功消除数据集泄漏，同时减少模型泄漏并保持高实用程序。可以在https://github.com/wearepal/realpatch上找到RealPatch的代码。